Import Dataset
#Import data
week_kuds <- read.csv("week_kuds2.2.csv", sep=";") #has the wrong week values
week_kuds3 <- read.csv("week_kuds1 - usar.csv", sep=";") #has the right week values
week_kuds$Week <- week_kuds3$Week #substitute the wrong for the right week in the dataset
#create week variable without the year
names(week_kuds)[names(week_kuds) == "Week"] <- "WeekYear"
week_kuds$Week <- substr(week_kuds$WeekYear, 1, 2)
#create year variable without the week
week_kuds$Year <- substr(week_kuds$WeekYear, 4, 7)
week_kuds$File <- as.factor(week_kuds$File)
week_kuds$Species <- as.factor(week_kuds$Species)
week_kuds$Transmitter <- as.factor(week_kuds$Transmitter)
week_kuds$KUD50 <- as.numeric(week_kuds$KUD50)
week_kuds$KUD95 <- as.numeric(week_kuds$KUD95)
week_kuds$Habitat <- as.factor(week_kuds$Habitat)
week_kuds$Migration <- as.factor(week_kuds$Migration)
week_kuds$ComImport <- as.factor(week_kuds$ComImport)
week_kuds$Length_cm <- as.numeric(week_kuds$Length_cm)
week_kuds$LengthStd <- as.numeric(week_kuds$LengthStd)
week_kuds$BodyMass <- as.numeric(week_kuds$BodyMass)
week_kuds$BodyMassStd <- as.numeric(week_kuds$BodyMassStd)
week_kuds$Longevity <- as.numeric(week_kuds$Longevity)
week_kuds$Vulnerability <- as.numeric(week_kuds$Vulnerability)
week_kuds$Troph <- as.numeric(week_kuds$Troph)
week_kuds$ReceiverDensity <- as.numeric(week_kuds$ReceiverDensity)
week_kuds$MonitArea_km2 <- as.numeric(week_kuds$MonitArea_km2)
week_kuds$MCP_km2 <- as.numeric(week_kuds$MCP_km2)
week_kuds$NReceivers <- as.numeric(week_kuds$NReceivers)
week_kuds$MaxDistReceivers <- as.numeric(week_kuds$MaxDistReceivers)
week_kuds$MaxLength <- as.numeric(week_kuds$MaxLength)
week_kuds$MaxBodyMass <- as.numeric(week_kuds$MaxBodyMass)
week_kuds$a <- as.numeric(week_kuds$a)
week_kuds$b <- as.numeric(week_kuds$b)
week_kuds$Week <- as.factor(week_kuds$Week)
week_kuds$Year <- as.factor(week_kuds$Year)
week_kuds$Spawn <- as.factor(week_kuds$Spawn)
week_kuds$Spawn <- with(week_kuds, ifelse((SpawnSeason == "SS" & Week %in% c("11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40")) |
(SpawnSeason == "A" & Week %in% c("41", "42", "43", "44", "45", "46", "47", "48", "49", "50")) |
(SpawnSeason == "W" & Week %in% c("51", "52", "53", "54", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10")),
"yes", "no"))
week_kuds$SpawnSeason <- as.factor(week_kuds$SpawnSeason)
boxplot(KUD95 ~ Spawn, data= week_kuds, col="deepskyblue")
boxplot(KUD50 ~ Spawn, data= week_kuds, col="green2")
#Comparar as médias dos KUDs dos individuos que se encontravam em época reprodutiva ou não
#escolhemos o teste wilcox porque não assume normalidade nos dados e é útil para grandes e pequenas amostras
wilcox.test(week_kuds$KUD95~week_kuds$Spawn) #de acordo com o teste realizado parece não haver evidências para afirmar que a home range varia consoante a Epoca Reprodutiva, visto que o p-value toma o valor de 0.5623 que é maior do que o nivel de significância 0.05
##
## Wilcoxon rank sum test with continuity correction
##
## data: week_kuds$KUD95 by week_kuds$Spawn
## W = 81731513, p-value = 0.5623
## alternative hypothesis: true location shift is not equal to 0
wilcox.test(week_kuds$KUD50~week_kuds$Spawn) #de acordo com o teste realizado parece não haver evidências para afirmar que a core area varia consoante a Epoca Reprodutiva, visto que o p-value toma o valor de 0.9972 que é maior do que o nivel de significância 0.05
##
## Wilcoxon rank sum test with continuity correction
##
## data: week_kuds$KUD50 by week_kuds$Spawn
## W = 81393886, p-value = 0.9972
## alternative hypothesis: true location shift is not equal to 0
glmm_total_kud95 <- glmmTMB(KUD95 ~ Spawn + MonitArea_km2 + (1|Species) + (1|Transmitter), data=week_kuds, family = Gamma(link="log"))
summary(glmm_total_kud95)
## Family: Gamma ( log )
## Formula:
## KUD95 ~ Spawn + MonitArea_km2 + (1 | Species) + (1 | Transmitter)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9739.4 9788.3 -4863.7 9727.4 25606
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Species (Intercept) 0.06316 0.2513
## Transmitter (Intercept) 0.08407 0.2899
## Number of obs: 25612, groups: Species, 30; Transmitter, 850
##
## Dispersion estimate for Gamma family (sigma^2): 0.0747
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.150202 0.055654 -2.699 0.00696 **
## Spawnyes 0.056691 0.003803 14.908 < 2e-16 ***
## MonitArea_km2 0.029942 0.002902 10.319 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
glmm_total_kud50 <- glmmTMB(KUD50 ~ Spawn + MonitArea_km2 + (1|Species) + (1|Transmitter), data=week_kuds, family = Gamma(link="log"))
summary(glmm_total_kud50)
## Family: Gamma ( log )
## Formula:
## KUD50 ~ Spawn + MonitArea_km2 + (1 | Species) + (1 | Transmitter)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76423.0 -76374.1 38217.5 -76435.0 25606
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Species (Intercept) 0.04558 0.2135
## Transmitter (Intercept) 0.07274 0.2697
## Number of obs: 25612, groups: Species, 30; Transmitter, 850
##
## Dispersion estimate for Gamma family (sigma^2): 0.0603
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.689189 0.048440 -34.87 <2e-16 ***
## Spawnyes 0.047096 0.003413 13.80 <2e-16 ***
## MonitArea_km2 0.023721 0.002684 8.84 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Analisys by File
# Divide dataset by 'File'
split_spawnFile <- split(week_kuds, week_kuds$File)
# Function to use the wilcoxon test in each sub-dataframe
wilcox_test_kud95 <- function(data) {
# Verify if Spawn has exactly 2 levels (yes and no)
if(length(unique(data$Spawn)) == 2) {
test_result <- wilcox.test(KUD95 ~ Spawn, data = data)
return(test_result$p.value)
} else {
return(NA) # Return NA if not
}
}
# Apply the function and get the p-values
p_values <- lapply(split_spawnFile, wilcox_test_kud95)
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
# Exhibit p-values
print(p_values)
## $Dactylopterus_volitans
## [1] NA
##
## $Dentex_dentex1
## [1] 0.01030334
##
## $Dentex_dentex2
## [1] 0.1078085
##
## $Dicentrarchus_labrax1
## [1] 1.450456e-05
##
## $Dicentrarchus_labrax2
## [1] 0.0001058427
##
## $Diplodus_cervinus
## [1] 0.7975494
##
## $Diplodus_sargus1
## [1] 0.1054672
##
## $Diplodus_sargus2
## [1] 0.00252126
##
## $Diplodus_sargus3
## [1] 0.002851269
##
## $Diplodus_sargus4
## [1] 0.224059
##
## $Diplodus_sargus5
## [1] 0.2519681
##
## $Diplodus_sargus6
## [1] 0.6146928
##
## $Diplodus_vulgaris1
## [1] 0.1348518
##
## $Diplodus_vulgaris2
## [1] 0.3333333
##
## $Epinephelus_marginatus1
## [1] 0.08732378
##
## $Epinephelus_marginatus2
## [1] 0.8344767
##
## $Epinephelus_marginatus3
## [1] 0.2898365
##
## $Epinephelus_marginatus4
## [1] 2.725643e-05
##
## $Gadus_morhua1
## [1] 3.51374e-06
##
## $Gadus_morhua2
## [1] 0.3779574
##
## $Gadus_morhua3
## [1] 3.646825e-09
##
## $Labrus_bergylta
## [1] 1.611826e-07
##
## $Lichia_amia
## [1] 0.03284102
##
## $Lithognathus_mormyrus
## [1] NA
##
## $Pagellus_erythrinus
## [1] NA
##
## $Pagrus_pagrus1
## [1] 0.1970555
##
## $Pagrus_pagrus2
## [1] 0.3868507
##
## $Pomatomus_saltatrix
## [1] 0.90633
##
## $Pseudocaranx_dentex
## [1] 0.074285
##
## $Sciaena_umbra1
## [1] 0.100855
##
## $Sciaena_umbra2
## [1] 0.0530303
##
## $Scorpaena_porcus
## [1] 0.02050939
##
## $Scorpaena_scrofa1
## [1] 0.5053349
##
## $Scorpaena_scrofa2
## [1] 4.234985e-05
##
## $Seriola_dumerili
## [1] 2.94866e-12
##
## $Seriola_rivoliana
## [1] 4.398831e-06
##
## $Serranus_atricauda
## [1] 6.274069e-08
##
## $Serranus_cabrilla
## [1] NA
##
## $Serranus_scriba
## [1] 0.8412007
##
## $Solea_senegalensis
## [1] 0.03581104
##
## $Sparisoma_cretense
## [1] 0.3443516
##
## $Sparus_aurata1
## [1] 0.434566
##
## $Sparus_aurata2
## [1] 0.1317029
##
## $Sphyraena_viridensis1
## [1] 0.0005515177
##
## $Sphyraena_viridensis2
## [1] 2.323167e-15
##
## $Spondyliosoma_cantharus
## [1] 5.783145e-05
##
## $Umbrina_cirrosa
## [1] NA
##
## $Xyrichtys_novacula
## [1] NA
p_values<- t(as.data.frame(p_values))
# Function to use the wilcoxon test in each sub-dataframe
wilcox_test_kud50 <- function(data) {
# Verify if Spawn has exactly 2 levels (yes and no)
if(length(unique(data$Spawn)) == 2) {
test_result <- wilcox.test(KUD50 ~ Spawn, data = data)
return(test_result$p.value)
} else {
return(NA) # Return NA if not
}
}
# Apply the function and get the p-values
p_values <- lapply(split_spawnFile, wilcox_test_kud50)
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
# Exhibit p-values
print(p_values)
## $Dactylopterus_volitans
## [1] NA
##
## $Dentex_dentex1
## [1] 0.0006766402
##
## $Dentex_dentex2
## [1] 0.07389496
##
## $Dicentrarchus_labrax1
## [1] 0.0007138528
##
## $Dicentrarchus_labrax2
## [1] 0.002072089
##
## $Diplodus_cervinus
## [1] 0.1787112
##
## $Diplodus_sargus1
## [1] 0.05129045
##
## $Diplodus_sargus2
## [1] 0.009404127
##
## $Diplodus_sargus3
## [1] 0.005536751
##
## $Diplodus_sargus4
## [1] 0.08955937
##
## $Diplodus_sargus5
## [1] 0.1154605
##
## $Diplodus_sargus6
## [1] 0.8877892
##
## $Diplodus_vulgaris1
## [1] 0.09801128
##
## $Diplodus_vulgaris2
## [1] 0.3333333
##
## $Epinephelus_marginatus1
## [1] 0.0007550945
##
## $Epinephelus_marginatus2
## [1] 0.8899593
##
## $Epinephelus_marginatus3
## [1] 0.360626
##
## $Epinephelus_marginatus4
## [1] 1.858381e-05
##
## $Gadus_morhua1
## [1] 8.959164e-05
##
## $Gadus_morhua2
## [1] 0.6036561
##
## $Gadus_morhua3
## [1] 4.300043e-09
##
## $Labrus_bergylta
## [1] 1.68144e-08
##
## $Lichia_amia
## [1] 0.2620757
##
## $Lithognathus_mormyrus
## [1] NA
##
## $Pagellus_erythrinus
## [1] NA
##
## $Pagrus_pagrus1
## [1] 0.04727474
##
## $Pagrus_pagrus2
## [1] 0.2663605
##
## $Pomatomus_saltatrix
## [1] 0.9564324
##
## $Pseudocaranx_dentex
## [1] 0.06130092
##
## $Sciaena_umbra1
## [1] 0.1403615
##
## $Sciaena_umbra2
## [1] 0.259324
##
## $Scorpaena_porcus
## [1] 0.01589705
##
## $Scorpaena_scrofa1
## [1] 0.8384118
##
## $Scorpaena_scrofa2
## [1] 0.0001346187
##
## $Seriola_dumerili
## [1] 7.89286e-12
##
## $Seriola_rivoliana
## [1] 0.1443612
##
## $Serranus_atricauda
## [1] 1.429118e-09
##
## $Serranus_cabrilla
## [1] NA
##
## $Serranus_scriba
## [1] 0.8428269
##
## $Solea_senegalensis
## [1] 0.1996681
##
## $Sparisoma_cretense
## [1] 0.6576976
##
## $Sparus_aurata1
## [1] 0.6422533
##
## $Sparus_aurata2
## [1] 0.005342033
##
## $Sphyraena_viridensis1
## [1] 0.001669869
##
## $Sphyraena_viridensis2
## [1] 6.294566e-07
##
## $Spondyliosoma_cantharus
## [1] 0.0001120269
##
## $Umbrina_cirrosa
## [1] NA
##
## $Xyrichtys_novacula
## [1] NA
p_values<- t(as.data.frame(p_values))
#Glmm KUD95 for each File
data_dentex_dentex1 <- subset(week_kuds, File == "Dentex_dentex1")
glmm_dentex_dentex1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dentex_dentex1, family = Gamma(link="log"))
summary(glmm_dentex_dentex1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex1
##
## AIC BIC logLik deviance df.resid
## 3.3 21.9 2.4 -4.7 774
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03892 0.1973
## Number of obs: 778, groups: Transmitter, 19
##
## Dispersion estimate for Gamma family (sigma^2): 0.0384
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.11417 0.04733 2.412 0.0159 *
## Spawnyes 0.06093 0.01442 4.227 2.37e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex1$KUD95 ~ data_dentex_dentex1$Spawn)
#################################################################################
data_dentex_dentex2 <- subset(week_kuds, File == "Dentex_dentex2")
glmm_dentex_dentex2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dentex_dentex2, family = Gamma(link="log"))
summary(glmm_dentex_dentex2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex2
##
## AIC BIC logLik deviance df.resid
## 1008.2 1025.8 -500.1 1000.2 595
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1173 0.3426
## Number of obs: 599, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.138
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.28149 0.09143 3.079 0.00208 **
## Spawnyes 0.15458 0.03177 4.865 1.14e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex2$KUD95 ~ data_dentex_dentex2$Spawn)
#################################################################################
data_dicentrarchus_labrax1 <- subset(week_kuds, File == "Dicentrarchus_labrax1")
glmm_dicentrarchus_labrax1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax1, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax1
##
## AIC BIC logLik deviance df.resid
## 753.4 772.4 -372.7 745.4 850
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1006 0.3171
## Number of obs: 854, groups: Transmitter, 93
##
## Dispersion estimate for Gamma family (sigma^2): 0.0931
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.16255 0.03588 4.531 5.88e-06 ***
## Spawnyes -0.04691 0.06031 -0.778 0.437
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax1$KUD95 ~ data_dicentrarchus_labrax1$Spawn)
#################################################################################
data_dicentrarchus_labrax2 <- subset(week_kuds, File == "Dicentrarchus_labrax2")
glmm_dicentrarchus_labrax2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax2, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax2
##
## AIC BIC logLik deviance df.resid
## 1620.9 1638.7 -806.4 1612.9 633
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.2229 0.4722
## Number of obs: 637, groups: Transmitter, 28
##
## Dispersion estimate for Gamma family (sigma^2): 0.188
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.62269 0.09675 6.436 1.22e-10 ***
## Spawnyes 0.26338 0.03995 6.593 4.31e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax2$KUD95 ~ data_dicentrarchus_labrax2$Spawn)
#################################################################################
data_diplodus_cervinus <- subset(week_kuds, File == "Diplodus_cervinus")
glmm_diplodus_cervinus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_cervinus, family = Gamma(link="log"))
summary(glmm_diplodus_cervinus)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_cervinus
##
## AIC BIC logLik deviance df.resid
## 164.8 175.0 -78.4 156.8 90
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.147 0.3834
## Number of obs: 94, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.15
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.31443 0.20324 1.547 0.12185
## Spawnyes -0.25631 0.09128 -2.808 0.00498 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_cervinus$KUD95 ~ data_diplodus_cervinus$Spawn)
#################################################################################
data_diplodus_sargus1 <- subset(week_kuds, File == "Diplodus_sargus1")
glmm_diplodus_sargus1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus1, family = Gamma(link="log"))
summary(glmm_diplodus_sargus1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus1
##
## AIC BIC logLik deviance df.resid
## 143.7 159.1 -67.8 135.7 347
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07295 0.2701
## Number of obs: 351, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.0723
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.042330 0.074203 0.571 0.568
## Spawnyes -0.009141 0.033723 -0.271 0.786
boxplot(data_diplodus_sargus1$KUD95 ~ data_diplodus_sargus1$Spawn)
#################################################################################
data_diplodus_sargus2 <- subset(week_kuds, File == "Diplodus_sargus2")
glmm_diplodus_sargus2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus2, family = Gamma(link="log"))
summary(glmm_diplodus_sargus2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus2
##
## AIC BIC logLik deviance df.resid
## -653.2 -635.2 330.6 -661.2 656
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.01889 0.1374
## Number of obs: 660, groups: Transmitter, 20
##
## Dispersion estimate for Gamma family (sigma^2): 0.0237
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.02555 0.03412 0.749 0.4539
## Spawnyes -0.02520 0.01480 -1.703 0.0886 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus2$KUD95 ~ data_diplodus_sargus2$Spawn)
#################################################################################
data_diplodus_sargus3 <- subset(week_kuds, File == "Diplodus_sargus3")
glmm_diplodus_sargus3 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus3, family = Gamma(link="log"))
summary(glmm_diplodus_sargus3)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus3
##
## AIC BIC logLik deviance df.resid
## -177.8 -168.2 92.9 -185.8 76
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0006858 0.02619
## Number of obs: 80, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.00797
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.22252 0.01878 -11.851 < 2e-16 ***
## Spawnyes 0.08364 0.02016 4.148 3.36e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus3$KUD95 ~ data_diplodus_sargus3$Spawn)
#################################################################################
data_diplodus_sargus4 <- subset(week_kuds, File == "Diplodus_sargus4")
glmm_diplodus_sargus4 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus4, family = Gamma(link="log"))
summary(glmm_diplodus_sargus4)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus4
##
## AIC BIC logLik deviance df.resid
## -1780.1 -1758.9 894.1 -1788.1 1470
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02426 0.1558
## Number of obs: 1474, groups: Transmitter, 41
##
## Dispersion estimate for Gamma family (sigma^2): 0.0172
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.055328 0.025343 -2.183 0.029 *
## Spawnyes -0.004623 0.006936 -0.666 0.505
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus4$KUD95 ~ data_diplodus_sargus4$Spawn)
#################################################################################
data_diplodus_sargus5 <- subset(week_kuds, File == "Diplodus_sargus5")
glmm_diplodus_sargus5 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus5, family = Gamma(link="log"))
summary(glmm_diplodus_sargus5)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus5
##
## AIC BIC logLik deviance df.resid
## 291.3 311.3 -141.6 283.3 1098
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02454 0.1566
## Number of obs: 1102, groups: Transmitter, 73
##
## Dispersion estimate for Gamma family (sigma^2): 0.0703
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.03582 0.02336 1.533 0.125
## Spawnyes -0.01763 0.01790 -0.985 0.325
boxplot(data_diplodus_sargus5$KUD95 ~ data_diplodus_sargus5$Spawn)
#################################################################################
data_diplodus_sargus6 <- subset(week_kuds, File == "Diplodus_sargus6")
glmm_diplodus_sargus6 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus6, family = Gamma(link="log"))
summary(glmm_diplodus_sargus6)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus6
##
## AIC BIC logLik deviance df.resid
## -37.4 -30.5 22.7 -45.4 37
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1828 0.4275
## Number of obs: 41, groups: Transmitter, 6
##
## Dispersion estimate for Gamma family (sigma^2): 0.0165
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.06001 0.17875 0.336 0.737
## Spawnyes -0.02549 0.05101 -0.500 0.617
boxplot(data_diplodus_sargus6$KUD95 ~ data_diplodus_sargus6$Spawn)
#################################################################################
data_diplodus_vulgaris1 <- subset(week_kuds, File == "Diplodus_vulgaris1")
glmm_diplodus_vulgaris1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris1, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris1
##
## AIC BIC logLik deviance df.resid
## -7.4 0.0 7.7 -15.4 42
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03967 0.1992
## Number of obs: 46, groups: Transmitter, 9
##
## Dispersion estimate for Gamma family (sigma^2): 0.0369
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0009159 0.0769672 0.012 0.9905
## Spawnyes -0.1871862 0.1024773 -1.827 0.0678 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris1$KUD95 ~ data_diplodus_vulgaris1$Spawn)
#################################################################################
data_diplodus_vulgaris2 <- subset(week_kuds, File == "Diplodus_vulgaris2")
glmm_diplodus_vulgaris2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris2, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris2
##
## AIC BIC logLik deviance df.resid
## -7.7 -10.1 7.8 -15.7 0
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.006177 0.07859
## Number of obs: 4, groups: Transmitter, 2
##
## Dispersion estimate for Gamma family (sigma^2): 7.18e-05
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.53980 0.05590 9.66 <2e-16 ***
## Spawnyes -0.72481 0.01044 -69.42 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris2$KUD95 ~ data_diplodus_vulgaris2$Spawn)
#################################################################################
data_epinephelus_marginatus1 <- subset(week_kuds, File == "Epinephelus_marginatus1")
glmm_epinephelus_marginatus1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus1, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus1
##
## AIC BIC logLik deviance df.resid
## -3878.8 -3856.3 1943.4 -3886.8 2051
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.004326 0.06577
## Number of obs: 2055, groups: Transmitter, 11
##
## Dispersion estimate for Gamma family (sigma^2): 0.0131
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.232142 0.020292 -11.440 < 2e-16 ***
## Spawnyes 0.035552 0.005102 6.969 3.2e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus1$KUD95 ~ data_epinephelus_marginatus1$Spawn)
#################################################################################
data_epinephelus_marginatus2 <- subset(week_kuds, File == "Epinephelus_marginatus2")
glmm_epinephelus_marginatus2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus2, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus2
##
## AIC BIC logLik deviance df.resid
## -1926.5 -1910.2 967.3 -1934.5 433
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 5.013e-05 0.00708
## Number of obs: 437, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.00114
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.260076 0.002685 -96.87 < 2e-16 ***
## Spawnyes 0.010431 0.003580 2.91 0.00358 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus2$KUD95 ~ data_epinephelus_marginatus2$Spawn)
#################################################################################
data_epinephelus_marginatus3 <- subset(week_kuds, File == "Epinephelus_marginatus3")
glmm_epinephelus_marginatus3 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus3, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus3)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus3
##
## AIC BIC logLik deviance df.resid
## -689.6 -675.9 348.8 -697.6 223
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0002354 0.01534
## Number of obs: 227, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.00425
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.249606 0.010156 -24.576 < 2e-16 ***
## Spawnyes 0.023242 0.008761 2.653 0.00798 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus3$KUD95 ~ data_epinephelus_marginatus3$Spawn)
#################################################################################
data_epinephelus_marginatus4 <- subset(week_kuds, File == "Epinephelus_marginatus4")
glmm_epinephelus_marginatus4 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus4, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus4)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus4
##
## AIC BIC logLik deviance df.resid
## 120.0 134.7 -56.0 112.0 289
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1211 0.348
## Number of obs: 293, groups: Transmitter, 17
##
## Dispersion estimate for Gamma family (sigma^2): 0.0814
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.10990 0.08870 -1.239 0.215
## Spawnyes 0.17134 0.03597 4.763 1.9e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus4$KUD95 ~ data_epinephelus_marginatus4$Spawn)
#################################################################################
data_gadus_morhua1 <- subset(week_kuds, File == "Gadus_morhua1")
glmm_gadus_morhua1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua1, family = Gamma(link="log"))
summary(glmm_gadus_morhua1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua1
##
## AIC BIC logLik deviance df.resid
## 307.2 328.8 -149.6 299.2 1631
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03343 0.1828
## Number of obs: 1635, groups: Transmitter, 60
##
## Dispersion estimate for Gamma family (sigma^2): 0.0566
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.01418 0.02752 0.515 0.606
## Spawnyes 0.07521 0.01442 5.217 1.82e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua1$KUD95 ~ data_gadus_morhua1$Spawn)
#################################################################################
data_gadus_morhua2 <- subset(week_kuds, File == "Gadus_morhua2")
glmm_gadus_morhua2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua2, family = Gamma(link="log"))
summary(glmm_gadus_morhua2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua2
##
## AIC BIC logLik deviance df.resid
## 197.1 217.2 -94.5 189.1 1132
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04499 0.2121
## Number of obs: 1136, groups: Transmitter, 56
##
## Dispersion estimate for Gamma family (sigma^2): 0.0684
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.004647 0.037490 -0.124 0.901
## Spawnyes -0.014876 0.024961 -0.596 0.551
boxplot(data_gadus_morhua2$KUD95 ~ data_gadus_morhua2$Spawn)
#################################################################################
data_gadus_morhua3 <- subset(week_kuds, File == "Gadus_morhua3")
glmm_gadus_morhua3 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua3, family = Gamma(link="log"))
summary(glmm_gadus_morhua3)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua3
##
## AIC BIC logLik deviance df.resid
## -741.7 -725.8 374.9 -749.7 395
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.004891 0.06994
## Number of obs: 399, groups: Transmitter, 29
##
## Dispersion estimate for Gamma family (sigma^2): 0.0111
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.17004 0.01833 -9.278 < 2e-16 ***
## Spawnyes 0.04018 0.01253 3.206 0.00135 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua3$KUD95 ~ data_gadus_morhua3$Spawn)
#################################################################################
data_labrus_bergylta <- subset(week_kuds, File == "Labrus_bergylta")
glmm_labrus_bergylta <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_labrus_bergylta, family = Gamma(link="log"))
summary(glmm_labrus_bergylta)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_labrus_bergylta
##
## AIC BIC logLik deviance df.resid
## -2897.4 -2878.7 1452.7 -2905.4 789
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.001729 0.04158
## Number of obs: 793, groups: Transmitter, 25
##
## Dispersion estimate for Gamma family (sigma^2): 0.00195
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.193686 0.008599 -22.523 < 2e-16 ***
## Spawnyes 0.022007 0.003162 6.959 3.42e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_labrus_bergylta$KUD95 ~ data_labrus_bergylta$Spawn)
#################################################################################
data_lichia_amia<- subset(week_kuds, File == "Lichia_amia")
glmm_lichia_amia <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_lichia_amia, family = Gamma(link="log"))
summary(glmm_lichia_amia)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_lichia_amia
##
## AIC BIC logLik deviance df.resid
## 87.5 92.8 -39.7 79.5 24
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 1.717e-11 4.144e-06
## Number of obs: 28, groups: Transmitter, 1
##
## Dispersion estimate for Gamma family (sigma^2): 0.0735
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.9167 0.1356 6.762 1.36e-11 ***
## Spawnyes 0.4826 0.1464 3.296 0.00098 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_lichia_amia$KUD95 ~ data_lichia_amia$Spawn)
#################################################################################
data_pagrus_pagrus1 <- subset(week_kuds, File == "Pagrus_pagrus1")
glmm_pagrus_pagrus1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus1, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus1
##
## AIC BIC logLik deviance df.resid
## -192.5 -174.8 100.3 -200.5 614
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05044 0.2246
## Number of obs: 618, groups: Transmitter, 20
##
## Dispersion estimate for Gamma family (sigma^2): 0.048
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.08623 0.05379 -1.603 0.10888
## Spawnyes 0.04813 0.01858 2.591 0.00957 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus1$KUD95 ~ data_pagrus_pagrus1$Spawn)
#################################################################################
data_pagrus_pagrus2 <- subset(week_kuds, File == "Pagrus_pagrus2")
glmm_pagrus_pagrus2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus2, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus2
##
## AIC BIC logLik deviance df.resid
## 17.3 23.6 -4.6 9.3 32
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.7809 0.8837
## Number of obs: 36, groups: Transmitter, 5
##
## Dispersion estimate for Gamma family (sigma^2): 0.0428
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.46922 0.40388 1.162 0.245
## Spawnyes 0.02786 0.07300 0.382 0.703
boxplot(data_pagrus_pagrus2$KUD95 ~ data_pagrus_pagrus2$Spawn)
#################################################################################
data_pomatomus_saltatrix <- subset(week_kuds, File == "Pomatomus_saltatrix")
glmm_pomatomus_saltatrix <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pomatomus_saltatrix, family = Gamma(link="log"))
summary(glmm_pomatomus_saltatrix)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pomatomus_saltatrix
##
## AIC BIC logLik deviance df.resid
## 622.8 635.1 -307.4 614.8 155
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05284 0.2299
## Number of obs: 159, groups: Transmitter, 8
##
## Dispersion estimate for Gamma family (sigma^2): 0.404
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.10622 0.10263 10.778 <2e-16 ***
## Spawnyes -0.05879 0.13605 -0.432 0.666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pomatomus_saltatrix$KUD95 ~ data_pomatomus_saltatrix$Spawn)
#################################################################################
data_pseudocaranx_dentex <- subset(week_kuds, File == "Pseudocaranx_dentex")
glmm_pseudocaranx_dentex <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pseudocaranx_dentex, family = Gamma(link="log"))
summary(glmm_pseudocaranx_dentex)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pseudocaranx_dentex
##
## AIC BIC logLik deviance df.resid
## 1718.0 1739.3 -855.0 1710.0 1523
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1472 0.3837
## Number of obs: 1527, groups: Transmitter, 31
##
## Dispersion estimate for Gamma family (sigma^2): 0.143
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.10946 0.07357 1.488 0.13681
## Spawnyes 0.06570 0.02074 3.168 0.00154 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pseudocaranx_dentex$KUD95 ~ data_pseudocaranx_dentex$Spawn)
#################################################################################
data_sciaena_umbra1 <- subset(week_kuds, File == "Sciaena_umbra1")
glmm_sciaena_umbra1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra1, family = Gamma(link="log"))
summary(glmm_sciaena_umbra1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra1
##
## AIC BIC logLik deviance df.resid
## -523.4 -512.0 265.7 -531.4 125
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.008098 0.08999
## Number of obs: 129, groups: Transmitter, 15
##
## Dispersion estimate for Gamma family (sigma^2): 0.000979
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.178216 0.024450 -7.289 3.12e-13 ***
## Spawnyes -0.009497 0.008092 -1.174 0.241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra1$KUD95 ~ data_sciaena_umbra1$Spawn)
#################################################################################
data_sciaena_umbra2 <- subset(week_kuds, File == "Sciaena_umbra2")
glmm_sciaena_umbra2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra2, family = Gamma(link="log"))
summary(glmm_sciaena_umbra2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra2
##
## AIC BIC logLik deviance df.resid
## 16.2 18.8 -4.1 8.2 10
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 4.245e-12 2.06e-06
## Number of obs: 14, groups: Transmitter, 1
##
## Dispersion estimate for Gamma family (sigma^2): 0.0555
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.48445 0.08901 5.443 5.25e-08 ***
## Spawnyes -0.29053 0.12588 -2.308 0.021 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra2$KUD95 ~ data_sciaena_umbra2$Spawn)
#################################################################################
data_scorpaena_porcus <- subset(week_kuds, File == "Scorpaena_porcus")
glmm_scorpaena_porcus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_porcus, family = Gamma(link="log"))
summary(glmm_scorpaena_porcus)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_porcus
##
## AIC BIC logLik deviance df.resid
## -143.2 -135.4 75.6 -151.2 48
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0005034 0.02244
## Number of obs: 52, groups: Transmitter, 13
##
## Dispersion estimate for Gamma family (sigma^2): 0.00441
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.17716 0.01605 -11.038 <2e-16 ***
## Spawnyes -0.04611 0.02097 -2.199 0.0279 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_porcus$KUD95 ~ data_scorpaena_porcus$Spawn)
#################################################################################
data_scorpaena_scrofa1 <- subset(week_kuds, File == "Scorpaena_scrofa1")
glmm_scorpaena_scrofa1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa1, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa1
##
## AIC BIC logLik deviance df.resid
## -202.8 -194.5 105.4 -210.8 54
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.002972 0.05451
## Number of obs: 58, groups: Transmitter, 7
##
## Dispersion estimate for Gamma family (sigma^2): 0.0017
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.18855 0.02615 -7.210 5.61e-13 ***
## Spawnyes -0.01773 0.01702 -1.042 0.297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa1$KUD95 ~ data_scorpaena_scrofa1$Spawn)
#################################################################################
data_scorpaena_scrofa2 <- subset(week_kuds, File == "Scorpaena_scrofa2")
glmm_scorpaena_scrofa2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa2, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa2
##
## AIC BIC logLik deviance df.resid
## -361.8 -344.9 184.9 -369.8 504
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0222 0.149
## Number of obs: 508, groups: Transmitter, 11
##
## Dispersion estimate for Gamma family (sigma^2): 0.0308
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.22672 0.04739 -4.784 1.72e-06 ***
## Spawnyes 0.16755 0.01625 10.313 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa2$KUD95 ~ data_scorpaena_scrofa2$Spawn)
#################################################################################
data_seriola_dumerili <- subset(week_kuds, File == "Seriola_dumerili")
glmm_seriola_dumerili <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_seriola_dumerili, family = Gamma(link="log"))
summary(glmm_seriola_dumerili)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_dumerili
##
## AIC BIC logLik deviance df.resid
## 1039.4 1054.9 -515.7 1031.4 352
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06693 0.2587
## Number of obs: 356, groups: Transmitter, 8
##
## Dispersion estimate for Gamma family (sigma^2): 0.2
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.67695 0.10122 6.688 2.26e-11 ***
## Spawnyes 0.42055 0.04952 8.493 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_dumerili$KUD95 ~ data_seriola_dumerili$Spawn)
#################################################################################
data_seriola_rivoliana <- subset(week_kuds, File == "Seriola_rivoliana")
glmm_seriola_rivoliana <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_seriola_rivoliana, family = Gamma(link="log"))
summary(glmm_seriola_rivoliana)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_rivoliana
##
## AIC BIC logLik deviance df.resid
## 1065.4 1089.2 -528.7 1057.4 2783
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.01212 0.1101
## Number of obs: 2787, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.0968
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.05972 0.02911 -2.052 0.0402 *
## Spawnyes 0.04774 0.01209 3.949 7.86e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_rivoliana$KUD95 ~ data_seriola_rivoliana$Spawn)
#################################################################################
data_serranus_atricauda <- subset(week_kuds, File == "Serranus_atricauda")
glmm_serranus_atricauda <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_serranus_atricauda, family = Gamma(link="log"))
summary(glmm_serranus_atricauda)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_atricauda
##
## AIC BIC logLik deviance df.resid
## -3445.6 -3427.7 1726.8 -3453.6 646
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 8.305e-05 0.009113
## Number of obs: 650, groups: Transmitter, 9
##
## Dispersion estimate for Gamma family (sigma^2): 0.000474
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.263748 0.003475 -75.89 <2e-16 ***
## Spawnyes -0.002961 0.001762 -1.68 0.0929 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_atricauda$KUD95 ~ data_serranus_atricauda$Spawn)
#################################################################################
data_serranus_scriba <- subset(week_kuds, File == "Serranus_scriba")
glmm_serranus_scriba <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_serranus_scriba, family = Gamma(link="log"))
summary(glmm_serranus_scriba)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_scriba
##
## AIC BIC logLik deviance df.resid
## -30.5 -25.3 19.3 -38.5 23
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02947 0.1717
## Number of obs: 27, groups: Transmitter, 10
##
## Dispersion estimate for Gamma family (sigma^2): 0.00803
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.116927 0.082518 -1.417 0.156
## Spawnyes -0.002848 0.063364 -0.045 0.964
boxplot(data_serranus_scriba$KUD95 ~ data_serranus_scriba$Spawn)
#################################################################################
data_solea_senegalensis <- subset(week_kuds, File == "Solea_senegalensis")
glmm_solea_senegalensis <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_solea_senegalensis, family = Gamma(link="log"))
summary(glmm_solea_senegalensis)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_solea_senegalensis
##
## AIC BIC logLik deviance df.resid
## -38.6 -24.7 23.3 -46.6 233
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05953 0.244
## Number of obs: 237, groups: Transmitter, 22
##
## Dispersion estimate for Gamma family (sigma^2): 0.0456
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.08365 0.05788 1.445 0.148
## Spawnyes -0.03383 0.03830 -0.883 0.377
boxplot(data_solea_senegalensis$KUD95 ~ data_solea_senegalensis$Spawn)
#################################################################################
data_sparisoma_cretense <- subset(week_kuds, File == "Sparisoma_cretense")
glmm_sparisoma_cretense <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparisoma_cretense, family = Gamma(link="log"))
summary(glmm_sparisoma_cretense)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparisoma_cretense
##
## AIC BIC logLik deviance df.resid
## -1031.2 -1012.6 519.6 -1039.2 765
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.01394 0.1181
## Number of obs: 769, groups: Transmitter, 10
##
## Dispersion estimate for Gamma family (sigma^2): 0.0195
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.117408 0.038447 -3.054 0.00226 **
## Spawnyes -0.002199 0.010253 -0.214 0.83017
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparisoma_cretense$KUD95 ~ data_sparisoma_cretense$Spawn)
#################################################################################
data_sparus_aurata1 <- subset(week_kuds, File == "Sparus_aurata1")
glmm_sparus_aurata1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparus_aurata1, family = Gamma(link="log"))
summary(glmm_sparus_aurata1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata1
##
## AIC BIC logLik deviance df.resid
## -517.9 -506.5 262.9 -525.9 123
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.001269 0.03563
## Number of obs: 127, groups: Transmitter, 7
##
## Dispersion estimate for Gamma family (sigma^2): 0.00129
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2336301 0.0149273 -15.651 <2e-16 ***
## Spawnyes 0.0008848 0.0076277 0.116 0.908
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata1$KUD95 ~ data_sparus_aurata1$Spawn)
#################################################################################
data_sparus_aurata2 <- subset(week_kuds, File == "Sparus_aurata2")
glmm_sparus_aurata2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparus_aurata2, family = Gamma(link="log"))
summary(glmm_sparus_aurata2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata2
##
## AIC BIC logLik deviance df.resid
## 1502.0 1522.2 -747.0 1494.0 1129
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.3298 0.5742
## Number of obs: 1133, groups: Transmitter, 43
##
## Dispersion estimate for Gamma family (sigma^2): 0.0997
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.60117 0.09069 6.629 3.37e-11 ***
## Spawnyes 0.02415 0.02409 1.002 0.316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata2$KUD95 ~ data_sparus_aurata2$Spawn)
#################################################################################
data_sphyraena_viridensis1 <- subset(week_kuds, File == "Sphyraena_viridensis1")
glmm_sphyraena_viridensis1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis1, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis1
##
## AIC BIC logLik deviance df.resid
## 159.8 180.5 -75.9 151.8 1294
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02137 0.1462
## Number of obs: 1298, groups: Transmitter, 13
##
## Dispersion estimate for Gamma family (sigma^2): 0.0778
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.053769 0.043035 -1.249 0.212
## Spawnyes 0.009959 0.015824 0.629 0.529
boxplot(data_sphyraena_viridensis1$KUD95 ~ data_sphyraena_viridensis1$Spawn)
#################################################################################
data_sphyraena_viridensis2 <- subset(week_kuds, File == "Sphyraena_viridensis2")
glmm_sphyraena_viridensis2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis2, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis2
##
## AIC BIC logLik deviance df.resid
## 1765.4 1782.7 -878.7 1757.4 554
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1546 0.3932
## Number of obs: 558, groups: Transmitter, 17
##
## Dispersion estimate for Gamma family (sigma^2): 0.277
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.51837 0.10258 5.053 4.34e-07 ***
## Spawnyes 0.56290 0.04677 12.037 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis2$KUD95 ~ data_sphyraena_viridensis2$Spawn)
#################################################################################
data_spondyliosoma_cantharus <- subset(week_kuds, File == "Spondyliosoma_cantharus")
glmm_spondyliosoma_cantharus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_spondyliosoma_cantharus, family = Gamma(link="log"))
summary(glmm_spondyliosoma_cantharus)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_spondyliosoma_cantharus
##
## AIC BIC logLik deviance df.resid
## 142.7 160.7 -67.4 134.7 659
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05021 0.2241
## Number of obs: 663, groups: Transmitter, 21
##
## Dispersion estimate for Gamma family (sigma^2): 0.0657
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.07551 0.05403 1.398 0.16219
## Spawnyes -0.05671 0.02129 -2.664 0.00773 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_spondyliosoma_cantharus$KUD95 ~ data_spondyliosoma_cantharus$Spawn)
#################################################################################
#Glmm KUD95 for each Species
data_dentex_dentex <- subset(week_kuds, Species == "Dden")
glmm_dentex_dentex <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dentex_dentex, family = Gamma(link="log"))
summary(glmm_dentex_dentex)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex
##
## AIC BIC logLik deviance df.resid
## 1297.4 1318.3 -644.7 1289.4 1373
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.08367 0.2893
## Number of obs: 1377, groups: Transmitter, 35
##
## Dispersion estimate for Gamma family (sigma^2): 0.0829
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.19110 0.05137 3.720 0.000199 ***
## Spawnyes 0.10072 0.01603 6.283 3.33e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex$KUD95 ~ data_dentex_dentex$Spawn)
#################################################################################
data_dicentrarchus_labrax <- subset(week_kuds, Species == "Dlab")
glmm_dicentrarchus_labrax <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax
##
## AIC BIC logLik deviance df.resid
## 2505.5 2526.7 -1248.8 2497.5 1487
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1702 0.4125
## Number of obs: 1491, groups: Transmitter, 121
##
## Dispersion estimate for Gamma family (sigma^2): 0.137
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.27403 0.04054 6.76 1.38e-11 ***
## Spawnyes 0.23486 0.03086 7.61 2.75e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax$KUD95 ~ data_dicentrarchus_labrax$Spawn)
#################################################################################
data_diplodus_cervinus <- subset(week_kuds, Species == "Dcer")
glmm_diplodus_cervinus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_cervinus, family = Gamma(link="log"))
summary(glmm_diplodus_cervinus)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_cervinus
##
## AIC BIC logLik deviance df.resid
## 164.8 175.0 -78.4 156.8 90
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.147 0.3834
## Number of obs: 94, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.15
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.31443 0.20324 1.547 0.12185
## Spawnyes -0.25631 0.09128 -2.808 0.00498 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_cervinus$KUD95 ~ data_diplodus_cervinus$Spawn)
#################################################################################
data_diplodus_sargus <- subset(week_kuds, Species == "Dsar")
glmm_diplodus_sargus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus, family = Gamma(link="log"))
summary(glmm_diplodus_sargus)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus
##
## AIC BIC logLik deviance df.resid
## -1391.2 -1366.3 699.6 -1399.2 3704
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03439 0.1854
## Number of obs: 3708, groups: Transmitter, 160
##
## Dispersion estimate for Gamma family (sigma^2): 0.039
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.002815 0.016056 0.175 0.861
## Spawnyes -0.008657 0.007103 -1.219 0.223
boxplot(data_diplodus_sargus$KUD95 ~ data_diplodus_sargus$Spawn)
#################################################################################
data_diplodus_vulgaris <- subset(week_kuds, Species == "Dvul")
glmm_diplodus_vulgaris <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris
##
## AIC BIC logLik deviance df.resid
## -0.5 7.1 4.3 -8.5 46
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05271 0.2296
## Number of obs: 50, groups: Transmitter, 11
##
## Dispersion estimate for Gamma family (sigma^2): 0.0396
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.06833 0.07991 0.855 0.39249
## Spawnyes -0.26811 0.09239 -2.902 0.00371 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris$KUD95 ~ data_diplodus_vulgaris$Spawn)
#################################################################################
data_epinephelus_marginatus <- subset(week_kuds, Species == "Emar")
glmm_epinephelus_marginatus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus
##
## AIC BIC logLik deviance df.resid
## -4624.1 -4600.1 2316.1 -4632.1 3008
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06052 0.246
## Number of obs: 3012, groups: Transmitter, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0175
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.18081 0.03585 -5.043 4.58e-07 ***
## Spawnyes 0.04345 0.00498 8.725 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus$KUD95 ~ data_epinephelus_marginatus$Spawn)
#################################################################################
data_gadus_morhua <- subset(week_kuds, Species == "Gmor")
glmm_gadus_morhua <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua, family = Gamma(link="log"))
summary(glmm_gadus_morhua)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua
##
## AIC BIC logLik deviance df.resid
## 136.8 161.0 -64.4 128.8 3166
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03853 0.1963
## Number of obs: 3170, groups: Transmitter, 145
##
## Dispersion estimate for Gamma family (sigma^2): 0.0555
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.03835 0.01973 -1.943 0.052 .
## Spawnyes 0.04808 0.01104 4.357 1.32e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua$KUD95 ~ data_gadus_morhua$Spawn)
#################################################################################
data_labrus_bergylta <- subset(week_kuds, Species == "Lber")
glmm_labrus_bergylta <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_labrus_bergylta, family = Gamma(link="log"))
summary(glmm_labrus_bergylta)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_labrus_bergylta
##
## AIC BIC logLik deviance df.resid
## -2897.4 -2878.7 1452.7 -2905.4 789
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.001729 0.04158
## Number of obs: 793, groups: Transmitter, 25
##
## Dispersion estimate for Gamma family (sigma^2): 0.00195
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.193686 0.008599 -22.523 < 2e-16 ***
## Spawnyes 0.022007 0.003162 6.959 3.42e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_labrus_bergylta$KUD95 ~ data_labrus_bergylta$Spawn)
#################################################################################
data_lichia_amia<- subset(week_kuds, Species == "Lami")
glmm_lichia_amia <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_lichia_amia, family = Gamma(link="log"))
summary(glmm_lichia_amia)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_lichia_amia
##
## AIC BIC logLik deviance df.resid
## 87.5 92.8 -39.7 79.5 24
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 1.717e-11 4.144e-06
## Number of obs: 28, groups: Transmitter, 1
##
## Dispersion estimate for Gamma family (sigma^2): 0.0735
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.9167 0.1356 6.762 1.36e-11 ***
## Spawnyes 0.4826 0.1464 3.296 0.00098 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_lichia_amia$KUD95 ~ data_lichia_amia$Spawn)
#################################################################################
data_pagrus_pagrus <- subset(week_kuds, Species == "Ppag")
glmm_pagrus_pagrus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus
##
## AIC BIC logLik deviance df.resid
## -159.5 -141.6 83.8 -167.5 650
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.2255 0.4748
## Number of obs: 654, groups: Transmitter, 25
##
## Dispersion estimate for Gamma family (sigma^2): 0.0479
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.01910 0.09772 0.195 0.84505
## Spawnyes 0.04928 0.01805 2.730 0.00634 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus$KUD95 ~ data_pagrus_pagrus$Spawn)
#################################################################################
data_pomatomus_saltatrix <- subset(week_kuds, Species == "Psal")
glmm_pomatomus_saltatrix <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pomatomus_saltatrix, family = Gamma(link="log"))
summary(glmm_pomatomus_saltatrix)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pomatomus_saltatrix
##
## AIC BIC logLik deviance df.resid
## 622.8 635.1 -307.4 614.8 155
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05284 0.2299
## Number of obs: 159, groups: Transmitter, 8
##
## Dispersion estimate for Gamma family (sigma^2): 0.404
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.10622 0.10263 10.778 <2e-16 ***
## Spawnyes -0.05879 0.13605 -0.432 0.666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pomatomus_saltatrix$KUD95 ~ data_pomatomus_saltatrix$Spawn)
#################################################################################
data_pseudocaranx_dentex <- subset(week_kuds, Species == "Pden")
glmm_pseudocaranx_dentex <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pseudocaranx_dentex, family = Gamma(link="log"))
summary(glmm_pseudocaranx_dentex)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pseudocaranx_dentex
##
## AIC BIC logLik deviance df.resid
## 1718.0 1739.3 -855.0 1710.0 1523
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1472 0.3837
## Number of obs: 1527, groups: Transmitter, 31
##
## Dispersion estimate for Gamma family (sigma^2): 0.143
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.10946 0.07357 1.488 0.13681
## Spawnyes 0.06570 0.02074 3.168 0.00154 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pseudocaranx_dentex$KUD95 ~ data_pseudocaranx_dentex$Spawn)
#################################################################################
data_sciaena_umbra <- subset(week_kuds, Species == "Sumb")
glmm_sciaena_umbra <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra, family = Gamma(link="log"))
summary(glmm_sciaena_umbra)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra
##
## AIC BIC logLik deviance df.resid
## -270.3 -258.5 139.2 -278.3 139
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02421 0.1556
## Number of obs: 143, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.00873
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.10460 0.04372 -2.393 0.01672 *
## Spawnyes -0.06285 0.02144 -2.931 0.00338 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra$KUD95 ~ data_sciaena_umbra$Spawn)
#################################################################################
data_scorpaena_porcus <- subset(week_kuds, Species == "Spor")
glmm_scorpaena_porcus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_porcus, family = Gamma(link="log"))
summary(glmm_scorpaena_porcus)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_porcus
##
## AIC BIC logLik deviance df.resid
## -143.2 -135.4 75.6 -151.2 48
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0005034 0.02244
## Number of obs: 52, groups: Transmitter, 13
##
## Dispersion estimate for Gamma family (sigma^2): 0.00441
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.17716 0.01605 -11.038 <2e-16 ***
## Spawnyes -0.04611 0.02097 -2.199 0.0279 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_porcus$KUD95 ~ data_scorpaena_porcus$Spawn)
#################################################################################
data_scorpaena_scrofa <- subset(week_kuds, Species == "Sscr")
glmm_scorpaena_scrofa <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa1
##
## AIC BIC logLik deviance df.resid
## -202.8 -194.5 105.4 -210.8 54
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.002972 0.05451
## Number of obs: 58, groups: Transmitter, 7
##
## Dispersion estimate for Gamma family (sigma^2): 0.0017
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.18855 0.02615 -7.210 5.61e-13 ***
## Spawnyes -0.01773 0.01702 -1.042 0.297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa$KUD95 ~ data_scorpaena_scrofa$Spawn)
#################################################################################
data_seriola_dumerili <- subset(week_kuds, Species == "Sdum")
glmm_seriola_dumerili <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_seriola_dumerili, family = Gamma(link="log"))
summary(glmm_seriola_dumerili)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_dumerili
##
## AIC BIC logLik deviance df.resid
## 1039.4 1054.9 -515.7 1031.4 352
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06693 0.2587
## Number of obs: 356, groups: Transmitter, 8
##
## Dispersion estimate for Gamma family (sigma^2): 0.2
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.67695 0.10122 6.688 2.26e-11 ***
## Spawnyes 0.42055 0.04952 8.493 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_dumerili$KUD95 ~ data_seriola_dumerili$Spawn)
#################################################################################
data_seriola_rivoliana <- subset(week_kuds, Species == "Sriv")
glmm_seriola_rivoliana <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_seriola_rivoliana, family = Gamma(link="log"))
summary(glmm_seriola_rivoliana)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_rivoliana
##
## AIC BIC logLik deviance df.resid
## 1065.4 1089.2 -528.7 1057.4 2783
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.01212 0.1101
## Number of obs: 2787, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.0968
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.05972 0.02911 -2.052 0.0402 *
## Spawnyes 0.04774 0.01209 3.949 7.86e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_rivoliana$KUD95 ~ data_seriola_rivoliana$Spawn)
#################################################################################
data_serranus_atricauda <- subset(week_kuds, Species == "Satr")
glmm_serranus_atricauda <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_serranus_atricauda, family = Gamma(link="log"))
summary(glmm_serranus_atricauda)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_atricauda
##
## AIC BIC logLik deviance df.resid
## -3445.6 -3427.7 1726.8 -3453.6 646
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 8.305e-05 0.009113
## Number of obs: 650, groups: Transmitter, 9
##
## Dispersion estimate for Gamma family (sigma^2): 0.000474
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.263748 0.003475 -75.89 <2e-16 ***
## Spawnyes -0.002961 0.001762 -1.68 0.0929 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_atricauda$KUD95 ~ data_serranus_atricauda$Spawn)
#################################################################################
data_serranus_scriba <- subset(week_kuds, Species == "Sscr")
glmm_serranus_scriba <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_serranus_scriba, family = Gamma(link="log"))
summary(glmm_serranus_scriba)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_scriba
##
## AIC BIC logLik deviance df.resid
## -30.5 -25.3 19.3 -38.5 23
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02947 0.1717
## Number of obs: 27, groups: Transmitter, 10
##
## Dispersion estimate for Gamma family (sigma^2): 0.00803
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.116927 0.082518 -1.417 0.156
## Spawnyes -0.002848 0.063364 -0.045 0.964
boxplot(data_serranus_scriba$KUD95 ~ data_serranus_scriba$Spawn)
#################################################################################
data_solea_senegalensis <- subset(week_kuds, Species == "Ssen")
glmm_solea_senegalensis <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_solea_senegalensis, family = Gamma(link="log"))
summary(glmm_solea_senegalensis)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_solea_senegalensis
##
## AIC BIC logLik deviance df.resid
## -38.6 -24.7 23.3 -46.6 233
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05953 0.244
## Number of obs: 237, groups: Transmitter, 22
##
## Dispersion estimate for Gamma family (sigma^2): 0.0456
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.08365 0.05788 1.445 0.148
## Spawnyes -0.03383 0.03830 -0.883 0.377
boxplot(data_solea_senegalensis$KUD95 ~ data_solea_senegalensis$Spawn)
#################################################################################
data_sparisoma_cretense <- subset(week_kuds, Species == "Scre")
glmm_sparisoma_cretense <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparisoma_cretense, family = Gamma(link="log"))
summary(glmm_sparisoma_cretense)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparisoma_cretense
##
## AIC BIC logLik deviance df.resid
## -1031.2 -1012.6 519.6 -1039.2 765
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.01394 0.1181
## Number of obs: 769, groups: Transmitter, 10
##
## Dispersion estimate for Gamma family (sigma^2): 0.0195
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.117408 0.038447 -3.054 0.00226 **
## Spawnyes -0.002199 0.010253 -0.214 0.83017
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparisoma_cretense$KUD95 ~ data_sparisoma_cretense$Spawn)
#################################################################################
data_sparus_aurata <- subset(week_kuds, Species == "Saur")
glmm_sparus_aurata <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparus_aurata, family = Gamma(link="log"))
summary(glmm_sparus_aurata)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata
##
## AIC BIC logLik deviance df.resid
## 1418.2 1438.8 -705.1 1410.2 1256
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.3676 0.6063
## Number of obs: 1260, groups: Transmitter, 50
##
## Dispersion estimate for Gamma family (sigma^2): 0.0901
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.48730 0.08823 5.523 3.33e-08 ***
## Spawnyes 0.02017 0.02158 0.935 0.35
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata$KUD95 ~ data_sparus_aurata$Spawn)
#################################################################################
data_sphyraena_viridensis <- subset(week_kuds, Species == "Svir")
glmm_sphyraena_viridensis <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis
##
## AIC BIC logLik deviance df.resid
## 2508.2 2530.3 -1250.1 2500.2 1852
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.2697 0.5194
## Number of obs: 1856, groups: Transmitter, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.152
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.33384 0.09657 3.457 0.000546 ***
## Spawnyes 0.16774 0.01869 8.974 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis$KUD95 ~ data_sphyraena_viridensis$Spawn)
#################################################################################
data_spondyliosoma_cantharus <- subset(week_kuds, Species == "Scan")
glmm_spondyliosoma_cantharus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_spondyliosoma_cantharus, family = Gamma(link="log"))
summary(glmm_spondyliosoma_cantharus)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_spondyliosoma_cantharus
##
## AIC BIC logLik deviance df.resid
## 142.7 160.7 -67.4 134.7 659
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05021 0.2241
## Number of obs: 663, groups: Transmitter, 21
##
## Dispersion estimate for Gamma family (sigma^2): 0.0657
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.07551 0.05403 1.398 0.16219
## Spawnyes -0.05671 0.02129 -2.664 0.00773 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_spondyliosoma_cantharus$KUD95 ~ data_spondyliosoma_cantharus$Spawn)
#################################################################################
#Glmm KUD50 for each Species
data_dentex_dentex <- subset(week_kuds, Species == "Dden")
glmm_dentex_dentex <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dentex_dentex, family = Gamma(link="log"))
summary(glmm_dentex_dentex)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex
##
## AIC BIC logLik deviance df.resid
## -3223.6 -3202.7 1615.8 -3231.6 1373
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06051 0.246
## Number of obs: 1377, groups: Transmitter, 35
##
## Dispersion estimate for Gamma family (sigma^2): 0.0744
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.37175 0.04412 -31.089 < 2e-16 ***
## Spawnyes 0.05748 0.01527 3.765 0.000166 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex$KUD50 ~ data_dentex_dentex$Spawn)
#################################################################################
data_dicentrarchus_labrax <- subset(week_kuds, Species == "Dlab")
glmm_dicentrarchus_labrax <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax
##
## AIC BIC logLik deviance df.resid
## -2559.1 -2537.9 1283.6 -2567.1 1487
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1448 0.3805
## Number of obs: 1491, groups: Transmitter, 121
##
## Dispersion estimate for Gamma family (sigma^2): 0.123
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.35503 0.03752 -36.12 < 2e-16 ***
## Spawnyes 0.19395 0.02920 6.64 3.1e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax$KUD50 ~ data_dicentrarchus_labrax$Spawn)
#################################################################################
data_diplodus_cervinus <- subset(week_kuds, Species == "Dcer")
glmm_diplodus_cervinus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_cervinus, family = Gamma(link="log"))
summary(glmm_diplodus_cervinus)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_cervinus
##
## AIC BIC logLik deviance df.resid
## -184.9 -174.7 96.4 -192.9 90
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1266 0.3558
## Number of obs: 94, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.0816
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.28557 0.18486 -6.954 3.55e-12 ***
## Spawnyes -0.13142 0.06793 -1.935 0.053 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_cervinus$KUD50 ~ data_diplodus_cervinus$Spawn)
#################################################################################
data_diplodus_sargus <- subset(week_kuds, Species == "Dsar")
glmm_diplodus_sargus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus, family = Gamma(link="log"))
summary(glmm_diplodus_sargus)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus
##
## AIC BIC logLik deviance df.resid
## -13164.3 -13139.4 6586.1 -13172.3 3704
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03508 0.1873
## Number of obs: 3708, groups: Transmitter, 160
##
## Dispersion estimate for Gamma family (sigma^2): 0.0352
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.560212 0.016077 -97.05 <2e-16 ***
## Spawnyes 0.009822 0.006760 1.45 0.146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus$KUD50 ~ data_diplodus_sargus$Spawn)
#################################################################################
data_diplodus_vulgaris <- subset(week_kuds, Species == "Dvul")
glmm_diplodus_vulgaris <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris
##
## AIC BIC logLik deviance df.resid
## -202.3 -194.6 105.1 -210.3 46
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03668 0.1915
## Number of obs: 50, groups: Transmitter, 11
##
## Dispersion estimate for Gamma family (sigma^2): 0.0166
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.57712 0.06353 -24.826 <2e-16 ***
## Spawnyes -0.13813 0.06039 -2.287 0.0222 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris$KUD50 ~ data_diplodus_vulgaris$Spawn)
#################################################################################
data_epinephelus_marginatus <- subset(week_kuds, Species == "Emar")
glmm_epinephelus_marginatus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus
##
## AIC BIC logLik deviance df.resid
## -15207.9 -15183.8 7607.9 -15215.9 3008
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03197 0.1788
## Number of obs: 3012, groups: Transmitter, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0115
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.731128 0.026115 -66.29 < 2e-16 ***
## Spawnyes 0.028028 0.004024 6.96 3.3e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus$KUD50 ~ data_epinephelus_marginatus$Spawn)
#################################################################################
data_gadus_morhua <- subset(week_kuds, Species == "Gmor")
glmm_gadus_morhua <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua, family = Gamma(link="log"))
summary(glmm_gadus_morhua)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua
##
## AIC BIC logLik deviance df.resid
## -9650.5 -9626.3 4829.3 -9658.5 3166
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03984 0.1996
## Number of obs: 3170, groups: Transmitter, 145
##
## Dispersion estimate for Gamma family (sigma^2): 0.0559
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.58936 0.01998 -79.54 < 2e-16 ***
## Spawnyes 0.04594 0.01107 4.15 3.31e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua$KUD50 ~ data_gadus_morhua$Spawn)
#################################################################################
data_labrus_bergylta <- subset(week_kuds, Species == "Lber")
glmm_labrus_bergylta <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_labrus_bergylta, family = Gamma(link="log"))
summary(glmm_labrus_bergylta)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_labrus_bergylta
##
## AIC BIC logLik deviance df.resid
## -5170.0 -5151.2 2589.0 -5178.0 789
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.002124 0.04609
## Number of obs: 793, groups: Transmitter, 25
##
## Dispersion estimate for Gamma family (sigma^2): 0.00238
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.727118 0.009530 -181.23 < 2e-16 ***
## Spawnyes 0.024741 0.003492 7.08 1.39e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_labrus_bergylta$KUD50 ~ data_labrus_bergylta$Spawn)
#################################################################################
data_lichia_amia<- subset(week_kuds, Species == "Lami")
glmm_lichia_amia <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_lichia_amia, family = Gamma(link="log"))
summary(glmm_lichia_amia)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_lichia_amia
##
## AIC BIC logLik deviance df.resid
## 5.2 10.5 1.4 -2.8 24
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 1.536e-12 1.239e-06
## Number of obs: 28, groups: Transmitter, 1
##
## Dispersion estimate for Gamma family (sigma^2): 0.109
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5626 0.1650 -3.409 0.000652 ***
## Spawnyes 0.2794 0.1782 1.567 0.117030
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_lichia_amia$KUD50 ~ data_lichia_amia$Spawn)
#################################################################################
data_pagrus_pagrus <- subset(week_kuds, Species == "Ppag")
glmm_pagrus_pagrus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus
##
## AIC BIC logLik deviance df.resid
## -2313.6 -2295.6 1160.8 -2321.6 650
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1991 0.4462
## Number of obs: 654, groups: Transmitter, 25
##
## Dispersion estimate for Gamma family (sigma^2): 0.0412
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.55372 0.09176 -16.933 <2e-16 ***
## Spawnyes 0.03840 0.01673 2.294 0.0218 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus$KUD50 ~ data_pagrus_pagrus$Spawn)
#################################################################################
data_pomatomus_saltatrix <- subset(week_kuds, Species == "Psal")
glmm_pomatomus_saltatrix <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pomatomus_saltatrix, family = Gamma(link="log"))
summary(glmm_pomatomus_saltatrix)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pomatomus_saltatrix
##
## AIC BIC logLik deviance df.resid
## 39.4 51.7 -15.7 31.4 155
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06272 0.2504
## Number of obs: 159, groups: Transmitter, 8
##
## Dispersion estimate for Gamma family (sigma^2): 0.368
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.69788 0.10724 -6.507 7.64e-11 ***
## Spawnyes -0.06845 0.13018 -0.526 0.599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pomatomus_saltatrix$KUD50 ~ data_pomatomus_saltatrix$Spawn)
#################################################################################
data_pseudocaranx_dentex <- subset(week_kuds, Species == "Pden")
glmm_pseudocaranx_dentex <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pseudocaranx_dentex, family = Gamma(link="log"))
summary(glmm_pseudocaranx_dentex)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pseudocaranx_dentex
##
## AIC BIC logLik deviance df.resid
## -3844.6 -3823.3 1926.3 -3852.6 1523
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.08244 0.2871
## Number of obs: 1527, groups: Transmitter, 31
##
## Dispersion estimate for Gamma family (sigma^2): 0.0955
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.53944 0.05554 -27.717 < 2e-16 ***
## Spawnyes 0.07720 0.01681 4.592 4.4e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pseudocaranx_dentex$KUD50 ~ data_pseudocaranx_dentex$Spawn)
#################################################################################
data_sciaena_umbra <- subset(week_kuds, Species == "Sumb")
glmm_sciaena_umbra <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra, family = Gamma(link="log"))
summary(glmm_sciaena_umbra)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra
##
## AIC BIC logLik deviance df.resid
## -754.1 -742.2 381.0 -762.1 139
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02172 0.1474
## Number of obs: 143, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.00628
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.64844 0.04060 -40.6 < 2e-16 ***
## Spawnyes -0.04746 0.01826 -2.6 0.00933 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra$KUD50 ~ data_sciaena_umbra$Spawn)
#################################################################################
data_scorpaena_porcus <- subset(week_kuds, Species == "Spor")
glmm_scorpaena_porcus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_porcus, family = Gamma(link="log"))
summary(glmm_scorpaena_porcus)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_porcus
##
## AIC BIC logLik deviance df.resid
## -282.9 -275.1 145.4 -290.9 48
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0004771 0.02184
## Number of obs: 52, groups: Transmitter, 13
##
## Dispersion estimate for Gamma family (sigma^2): 0.00659
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.70114 0.01877 -90.63 <2e-16 ***
## Spawnyes -0.05518 0.02466 -2.24 0.0253 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_porcus$KUD50 ~ data_scorpaena_porcus$Spawn)
#################################################################################
data_scorpaena_scrofa <- subset(week_kuds, Species == "Sscr")
glmm_scorpaena_scrofa <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa1
##
## AIC BIC logLik deviance df.resid
## -202.8 -194.5 105.4 -210.8 54
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.002972 0.05451
## Number of obs: 58, groups: Transmitter, 7
##
## Dispersion estimate for Gamma family (sigma^2): 0.0017
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.18855 0.02615 -7.210 5.61e-13 ***
## Spawnyes -0.01773 0.01702 -1.042 0.297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa$KUD95 ~ data_scorpaena_scrofa$Spawn)
#################################################################################
data_seriola_dumerili <- subset(week_kuds, Species == "Sdum")
glmm_seriola_dumerili <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_seriola_dumerili, family = Gamma(link="log"))
summary(glmm_seriola_dumerili)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_dumerili
##
## AIC BIC logLik deviance df.resid
## -169.4 -153.9 88.7 -177.4 352
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04089 0.2022
## Number of obs: 356, groups: Transmitter, 8
##
## Dispersion estimate for Gamma family (sigma^2): 0.202
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.02861 0.08266 -12.444 <2e-16 ***
## Spawnyes 0.42232 0.04948 8.535 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_dumerili$KUD50 ~ data_seriola_dumerili$Spawn)
#################################################################################
data_seriola_rivoliana <- subset(week_kuds, Species == "Sriv")
glmm_seriola_rivoliana <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_seriola_rivoliana, family = Gamma(link="log"))
summary(glmm_seriola_rivoliana)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_rivoliana
##
## AIC BIC logLik deviance df.resid
## -9159.6 -9135.8 4583.8 -9167.6 2783
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.006496 0.0806
## Number of obs: 2787, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.0613
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.667018 0.021514 -77.49 < 2e-16 ***
## Spawnyes 0.028314 0.009618 2.94 0.00324 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_rivoliana$KUD50 ~ data_seriola_rivoliana$Spawn)
#################################################################################
data_serranus_atricauda <- subset(week_kuds, Species == "Satr")
glmm_serranus_atricauda <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_serranus_atricauda, family = Gamma(link="log"))
summary(glmm_serranus_atricauda)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_atricauda
##
## AIC BIC logLik deviance df.resid
## -5402.6 -5384.7 2705.3 -5410.6 646
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 9.271e-05 0.009629
## Number of obs: 650, groups: Transmitter, 9
##
## Dispersion estimate for Gamma family (sigma^2): 0.000501
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.796127 0.003653 -491.7 <2e-16 ***
## Spawnyes -0.004663 0.001812 -2.6 0.0101 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_atricauda$KUD50 ~ data_serranus_atricauda$Spawn)
#################################################################################
data_serranus_scriba <- subset(week_kuds, Species == "Sscr")
glmm_serranus_scriba <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_serranus_scriba, family = Gamma(link="log"))
summary(glmm_serranus_scriba)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_scriba
##
## AIC BIC logLik deviance df.resid
## -111.0 -105.8 59.5 -119.0 23
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02159 0.1469
## Number of obs: 27, groups: Transmitter, 10
##
## Dispersion estimate for Gamma family (sigma^2): 0.0111
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.72592 0.08615 -20.033 <2e-16 ***
## Spawnyes 0.06062 0.07423 0.817 0.414
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_scriba$KUD50 ~ data_serranus_scriba$Spawn)
#################################################################################
data_solea_senegalensis <- subset(week_kuds, Species == "Ssen")
glmm_solea_senegalensis <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_solea_senegalensis, family = Gamma(link="log"))
summary(glmm_solea_senegalensis)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_solea_senegalensis
##
## AIC BIC logLik deviance df.resid
## -794.3 -780.5 401.2 -802.3 233
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04342 0.2084
## Number of obs: 237, groups: Transmitter, 22
##
## Dispersion estimate for Gamma family (sigma^2): 0.0426
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.49352 0.05066 -29.481 <2e-16 ***
## Spawnyes -0.03657 0.03680 -0.994 0.32
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_solea_senegalensis$KUD50 ~ data_solea_senegalensis$Spawn)
#################################################################################
data_sparisoma_cretense <- subset(week_kuds, Species == "Scre")
glmm_sparisoma_cretense <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparisoma_cretense, family = Gamma(link="log"))
summary(glmm_sparisoma_cretense)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparisoma_cretense
##
## AIC BIC logLik deviance df.resid
## -3587.9 -3569.3 1797.9 -3595.9 765
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02709 0.1646
## Number of obs: 769, groups: Transmitter, 10
##
## Dispersion estimate for Gamma family (sigma^2): 0.0153
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.662623 0.052688 -31.556 <2e-16 ***
## Spawnyes 0.004549 0.009085 0.501 0.617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparisoma_cretense$KUD50 ~ data_sparisoma_cretense$Spawn)
#################################################################################
data_sparus_aurata <- subset(week_kuds, Species == "Saur")
glmm_sparus_aurata <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparus_aurata, family = Gamma(link="log"))
summary(glmm_sparus_aurata)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata
##
## AIC BIC logLik deviance df.resid
## -2574.4 -2553.8 1291.2 -2582.4 1256
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.3093 0.5561
## Number of obs: 1260, groups: Transmitter, 50
##
## Dispersion estimate for Gamma family (sigma^2): 0.0901
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.12109 0.08132 -13.786 <2e-16 ***
## Spawnyes 0.04739 0.02160 2.194 0.0282 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata$KUD50 ~ data_sparus_aurata$Spawn)
#################################################################################
data_sphyraena_viridensis <- subset(week_kuds, Species == "Svir")
glmm_sphyraena_viridensis <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis
##
## AIC BIC logLik deviance df.resid
## -4302.9 -4280.8 2155.5 -4310.9 1852
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.192 0.4382
## Number of obs: 1856, groups: Transmitter, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.103
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.31015 0.08139 -16.097 < 2e-16 ***
## Spawnyes 0.09604 0.01525 6.297 3.04e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis$KUD50 ~ data_sphyraena_viridensis$Spawn)
#################################################################################
data_spondyliosoma_cantharus <- subset(week_kuds, Species == "Scan")
glmm_spondyliosoma_cantharus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_spondyliosoma_cantharus, family = Gamma(link="log"))
summary(glmm_spondyliosoma_cantharus)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_spondyliosoma_cantharus
##
## AIC BIC logLik deviance df.resid
## -1988.4 -1970.4 998.2 -1996.4 659
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06214 0.2493
## Number of obs: 663, groups: Transmitter, 21
##
## Dispersion estimate for Gamma family (sigma^2): 0.0586
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.46186 0.05876 -24.877 < 2e-16 ***
## Spawnyes -0.05414 0.02015 -2.687 0.00721 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_spondyliosoma_cantharus$KUD50 ~ data_spondyliosoma_cantharus$Spawn)
#################################################################################
#Glmm KUD50 for each File
data_dentex_dentex1 <- subset(week_kuds, File == "Dentex_dentex1")
glmm_dentex_dentex1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dentex_dentex1, family = Gamma(link="log"))
summary(glmm_dentex_dentex1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex1
##
## AIC BIC logLik deviance df.resid
## -2280.3 -2261.7 1144.1 -2288.3 774
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04613 0.2148
## Number of obs: 778, groups: Transmitter, 19
##
## Dispersion estimate for Gamma family (sigma^2): 0.0446
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.43928 0.05150 -27.948 < 2e-16 ***
## Spawnyes 0.08437 0.01557 5.418 6.02e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex1$KUD50 ~ data_dentex_dentex1$Spawn)
#################################################################################
data_dentex_dentex2 <- subset(week_kuds, File == "Dentex_dentex2")
glmm_dentex_dentex2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dentex_dentex2, family = Gamma(link="log"))
summary(glmm_dentex_dentex2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex2
##
## AIC BIC logLik deviance df.resid
## -1086.3 -1068.7 547.2 -1094.3 595
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07066 0.2658
## Number of obs: 599, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.112
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.29424 0.07216 -17.935 <2e-16 ***
## Spawnyes 0.02110 0.02875 0.734 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex2$KUD50 ~ data_dentex_dentex2$Spawn)
#################################################################################
data_dicentrarchus_labrax1 <- subset(week_kuds, File == "Dicentrarchus_labrax1")
glmm_dicentrarchus_labrax1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax1, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax1
##
## AIC BIC logLik deviance df.resid
## -2194.2 -2175.2 1101.1 -2202.2 850
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07913 0.2813
## Number of obs: 854, groups: Transmitter, 93
##
## Dispersion estimate for Gamma family (sigma^2): 0.0766
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.46528 0.03192 -45.91 <2e-16 ***
## Spawnyes -0.01360 0.05476 -0.25 0.804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax1$KUD50 ~ data_dicentrarchus_labrax1$Spawn)
#################################################################################
data_dicentrarchus_labrax2 <- subset(week_kuds, File == "Dicentrarchus_labrax2")
glmm_dicentrarchus_labrax2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax2, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax2
##
## AIC BIC logLik deviance df.resid
## -534.6 -516.8 271.3 -542.6 633
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1997 0.4469
## Number of obs: 637, groups: Transmitter, 28
##
## Dispersion estimate for Gamma family (sigma^2): 0.179
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.00901 0.09191 -10.978 < 2e-16 ***
## Spawnyes 0.20774 0.03891 5.338 9.37e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax2$KUD50 ~ data_dicentrarchus_labrax2$Spawn)
#################################################################################
data_diplodus_cervinus <- subset(week_kuds, File == "Diplodus_cervinus")
glmm_diplodus_cervinus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_cervinus, family = Gamma(link="log"))
summary(glmm_diplodus_cervinus)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_cervinus
##
## AIC BIC logLik deviance df.resid
## -184.9 -174.7 96.4 -192.9 90
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1266 0.3558
## Number of obs: 94, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.0816
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.28557 0.18486 -6.954 3.55e-12 ***
## Spawnyes -0.13142 0.06793 -1.935 0.053 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_cervinus$KUD50 ~ data_diplodus_cervinus$Spawn)
#################################################################################
data_diplodus_sargus1 <- subset(week_kuds, File == "Diplodus_sargus1")
glmm_diplodus_sargus1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus1, family = Gamma(link="log"))
summary(glmm_diplodus_sargus1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus1
##
## AIC BIC logLik deviance df.resid
## -1007.6 -992.1 507.8 -1015.6 347
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06204 0.2491
## Number of obs: 351, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.0617
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.53034 0.06840 -22.372 <2e-16 ***
## Spawnyes 0.00321 0.03104 0.103 0.918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus1$KUD50 ~ data_diplodus_sargus1$Spawn)
#################################################################################
data_diplodus_sargus2 <- subset(week_kuds, File == "Diplodus_sargus2")
glmm_diplodus_sargus2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus2, family = Gamma(link="log"))
summary(glmm_diplodus_sargus2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus2
##
## AIC BIC logLik deviance df.resid
## -2444.7 -2426.7 1226.4 -2452.7 656
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02645 0.1626
## Number of obs: 660, groups: Transmitter, 20
##
## Dispersion estimate for Gamma family (sigma^2): 0.0333
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.48767 0.04036 -36.86 <2e-16 ***
## Spawnyes -0.04083 0.01751 -2.33 0.0197 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus2$KUD50 ~ data_diplodus_sargus2$Spawn)
#################################################################################
data_diplodus_sargus3 <- subset(week_kuds, File == "Diplodus_sargus3")
glmm_diplodus_sargus3 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus3, family = Gamma(link="log"))
summary(glmm_diplodus_sargus3)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus3
##
## AIC BIC logLik deviance df.resid
## -403.3 -393.7 205.6 -411.3 76
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0006003 0.0245
## Number of obs: 80, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.0104
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.76106 0.01966 -89.58 < 2e-16 ***
## Spawnyes 0.08940 0.02304 3.88 0.000104 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus3$KUD50 ~ data_diplodus_sargus3$Spawn)
#################################################################################
data_diplodus_sargus4 <- subset(week_kuds, File == "Diplodus_sargus4")
glmm_diplodus_sargus4 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus4, family = Gamma(link="log"))
summary(glmm_diplodus_sargus4)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus4
##
## AIC BIC logLik deviance df.resid
## -6143.6 -6122.4 3075.8 -6151.6 1470
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03507 0.1873
## Number of obs: 1474, groups: Transmitter, 41
##
## Dispersion estimate for Gamma family (sigma^2): 0.0185
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.579419 0.030197 -52.30 <2e-16 ***
## Spawnyes 0.001954 0.007205 0.27 0.786
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus4$KUD50 ~ data_diplodus_sargus4$Spawn)
#################################################################################
data_diplodus_sargus5 <- subset(week_kuds, File == "Diplodus_sargus5")
glmm_diplodus_sargus5 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus5, family = Gamma(link="log"))
summary(glmm_diplodus_sargus5)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus5
##
## AIC BIC logLik deviance df.resid
## -3438.5 -3418.4 1723.2 -3446.5 1098
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02855 0.169
## Number of obs: 1102, groups: Transmitter, 73
##
## Dispersion estimate for Gamma family (sigma^2): 0.0532
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.56587 0.02359 -66.39 <2e-16 ***
## Spawnyes 0.03946 0.01575 2.51 0.0122 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus5$KUD50 ~ data_diplodus_sargus5$Spawn)
#################################################################################
data_diplodus_sargus6 <- subset(week_kuds, File == "Diplodus_sargus6")
glmm_diplodus_sargus6 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus6, family = Gamma(link="log"))
summary(glmm_diplodus_sargus6)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus6
##
## AIC BIC logLik deviance df.resid
## -223.1 -216.2 115.5 -231.1 37
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.08092 0.2845
## Number of obs: 41, groups: Transmitter, 6
##
## Dispersion estimate for Gamma family (sigma^2): 0.00378
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.60644 0.11761 -13.659 <2e-16 ***
## Spawnyes 0.03602 0.02452 1.469 0.142
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus6$KUD50 ~ data_diplodus_sargus6$Spawn)
#################################################################################
data_diplodus_vulgaris1 <- subset(week_kuds, File == "Diplodus_vulgaris1")
glmm_diplodus_vulgaris1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris1, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris1
##
## AIC BIC logLik deviance df.resid
## -195.6 -188.3 101.8 -203.6 42
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04232 0.2057
## Number of obs: 46, groups: Transmitter, 9
##
## Dispersion estimate for Gamma family (sigma^2): 0.0133
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.60890 0.07275 -22.115 <2e-16 ***
## Spawnyes -0.05592 0.06168 -0.907 0.365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris1$KUD50 ~ data_diplodus_vulgaris1$Spawn)
#################################################################################
data_diplodus_vulgaris2 <- subset(week_kuds, File == "Diplodus_vulgaris2")
glmm_diplodus_vulgaris2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris2, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris2
##
## AIC BIC logLik deviance df.resid
## -26.9 -29.3 17.4 -34.9 0
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.002723 0.05218
## Number of obs: 4, groups: Transmitter, 2
##
## Dispersion estimate for Gamma family (sigma^2): 9.14e-06
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.283298 0.036960 -34.72 <2e-16 ***
## Spawnyes -0.567608 0.003708 -153.06 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris2$KUD50 ~ data_diplodus_vulgaris2$Spawn)
#################################################################################
data_epinephelus_marginatus1 <- subset(week_kuds, File == "Epinephelus_marginatus1")
glmm_epinephelus_marginatus1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus1, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus1
##
## AIC BIC logLik deviance df.resid
## -10604.1 -10581.6 5306.0 -10612.1 2051
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.005139 0.07169
## Number of obs: 2055, groups: Transmitter, 11
##
## Dispersion estimate for Gamma family (sigma^2): 0.0109
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.767219 0.021966 -80.45 < 2e-16 ***
## Spawnyes 0.020935 0.004637 4.51 6.34e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus1$KUD50 ~ data_epinephelus_marginatus1$Spawn)
#################################################################################
data_epinephelus_marginatus2 <- subset(week_kuds, File == "Epinephelus_marginatus2")
glmm_epinephelus_marginatus2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus2, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus2
##
## AIC BIC logLik deviance df.resid
## -3693.3 -3677.0 1850.6 -3701.3 433
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 4.253e-05 0.006522
## Number of obs: 437, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.000423
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.794331 0.002075 -864.6 < 2e-16 ***
## Spawnyes 0.007375 0.002189 3.4 0.000755 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus2$KUD50 ~ data_epinephelus_marginatus2$Spawn)
#################################################################################
data_epinephelus_marginatus3 <- subset(week_kuds, File == "Epinephelus_marginatus3")
glmm_epinephelus_marginatus3 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus3, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus3)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus3
##
## AIC BIC logLik deviance df.resid
## -1368.5 -1354.8 688.2 -1376.5 223
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0002907 0.01705
## Number of obs: 227, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.00461
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.785956 0.010985 -162.58 < 2e-16 ***
## Spawnyes 0.023495 0.009119 2.58 0.00999 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus3$KUD50 ~ data_epinephelus_marginatus3$Spawn)
#################################################################################
data_epinephelus_marginatus4 <- subset(week_kuds, File == "Epinephelus_marginatus4")
glmm_epinephelus_marginatus4 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus4, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus4)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus4
##
## AIC BIC logLik deviance df.resid
## -1033.4 -1018.7 520.7 -1041.4 289
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06315 0.2513
## Number of obs: 293, groups: Transmitter, 17
##
## Dispersion estimate for Gamma family (sigma^2): 0.0367
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.67263 0.06362 -26.29 < 2e-16 ***
## Spawnyes 0.11494 0.02389 4.81 1.51e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus4$KUD50 ~ data_epinephelus_marginatus4$Spawn)
#################################################################################
data_gadus_morhua1 <- subset(week_kuds, File == "Gadus_morhua1")
glmm_gadus_morhua1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua1, family = Gamma(link="log"))
summary(glmm_gadus_morhua1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua1
##
## AIC BIC logLik deviance df.resid
## -4354.4 -4332.8 2181.2 -4362.4 1631
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04348 0.2085
## Number of obs: 1635, groups: Transmitter, 60
##
## Dispersion estimate for Gamma family (sigma^2): 0.0697
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.51844 0.03117 -48.72 < 2e-16 ***
## Spawnyes 0.08095 0.01598 5.07 4.08e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua1$KUD50 ~ data_gadus_morhua1$Spawn)
#################################################################################
data_gadus_morhua2 <- subset(week_kuds, File == "Gadus_morhua2")
glmm_gadus_morhua2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua2, family = Gamma(link="log"))
summary(glmm_gadus_morhua2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua2
##
## AIC BIC logLik deviance df.resid
## -3787.9 -3767.8 1898.0 -3795.9 1132
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03061 0.175
## Number of obs: 1136, groups: Transmitter, 56
##
## Dispersion estimate for Gamma family (sigma^2): 0.0486
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.57239 0.03120 -50.40 <2e-16 ***
## Spawnyes -0.04405 0.02104 -2.09 0.0363 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua2$KUD50 ~ data_gadus_morhua2$Spawn)
#################################################################################
data_gadus_morhua3 <- subset(week_kuds, File == "Gadus_morhua3")
glmm_gadus_morhua3 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua3, family = Gamma(link="log"))
summary(glmm_gadus_morhua3)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua3
##
## AIC BIC logLik deviance df.resid
## -1819.0 -1803.0 913.5 -1827.0 395
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.008528 0.09235
## Number of obs: 399, groups: Transmitter, 29
##
## Dispersion estimate for Gamma family (sigma^2): 0.0158
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.70272 0.02317 -73.47 < 2e-16 ***
## Spawnyes 0.05523 0.01494 3.70 0.000219 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua3$KUD50 ~ data_gadus_morhua3$Spawn)
#################################################################################
data_labrus_bergylta <- subset(week_kuds, File == "Labrus_bergylta")
glmm_labrus_bergylta <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_labrus_bergylta, family = Gamma(link="log"))
summary(glmm_labrus_bergylta)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_labrus_bergylta
##
## AIC BIC logLik deviance df.resid
## -5170.0 -5151.2 2589.0 -5178.0 789
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.002124 0.04609
## Number of obs: 793, groups: Transmitter, 25
##
## Dispersion estimate for Gamma family (sigma^2): 0.00238
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.727118 0.009530 -181.23 < 2e-16 ***
## Spawnyes 0.024741 0.003492 7.08 1.39e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_labrus_bergylta$KUD50 ~ data_labrus_bergylta$Spawn)
#################################################################################
data_lichia_amia<- subset(week_kuds, File == "Lichia_amia")
glmm_lichia_amia <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_lichia_amia, family = Gamma(link="log"))
summary(glmm_lichia_amia)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_lichia_amia
##
## AIC BIC logLik deviance df.resid
## 5.2 10.5 1.4 -2.8 24
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 1.536e-12 1.239e-06
## Number of obs: 28, groups: Transmitter, 1
##
## Dispersion estimate for Gamma family (sigma^2): 0.109
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5626 0.1650 -3.409 0.000652 ***
## Spawnyes 0.2794 0.1782 1.567 0.117030
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_lichia_amia$KUD50 ~ data_lichia_amia$Spawn)
#################################################################################
data_pagrus_pagrus1 <- subset(week_kuds, File == "Pagrus_pagrus1")
glmm_pagrus_pagrus1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus1, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus1
##
## AIC BIC logLik deviance df.resid
## -2241.5 -2223.8 1124.8 -2249.5 614
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02536 0.1593
## Number of obs: 618, groups: Transmitter, 20
##
## Dispersion estimate for Gamma family (sigma^2): 0.0413
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.66357 0.03941 -42.22 <2e-16 ***
## Spawnyes 0.03767 0.01717 2.19 0.0283 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus1$KUD50 ~ data_pagrus_pagrus1$Spawn)
#################################################################################
data_pagrus_pagrus2 <- subset(week_kuds, File == "Pagrus_pagrus2")
glmm_pagrus_pagrus2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus2, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus2
##
## AIC BIC logLik deviance df.resid
## -97.9 -91.6 53.0 -105.9 32
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.7335 0.8565
## Number of obs: 36, groups: Transmitter, 5
##
## Dispersion estimate for Gamma family (sigma^2): 0.0373
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.083803 0.390730 -2.774 0.00554 **
## Spawnyes 0.007209 0.068040 0.106 0.91562
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus2$KUD50 ~ data_pagrus_pagrus2$Spawn)
#################################################################################
data_pomatomus_saltatrix <- subset(week_kuds, File == "Pomatomus_saltatrix")
glmm_pomatomus_saltatrix <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pomatomus_saltatrix, family = Gamma(link="log"))
summary(glmm_pomatomus_saltatrix)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pomatomus_saltatrix
##
## AIC BIC logLik deviance df.resid
## 39.4 51.7 -15.7 31.4 155
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06272 0.2504
## Number of obs: 159, groups: Transmitter, 8
##
## Dispersion estimate for Gamma family (sigma^2): 0.368
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.69788 0.10724 -6.507 7.64e-11 ***
## Spawnyes -0.06845 0.13018 -0.526 0.599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pomatomus_saltatrix$KUD50 ~ data_pomatomus_saltatrix$Spawn)
#################################################################################
data_pseudocaranx_dentex <- subset(week_kuds, File == "Pseudocaranx_dentex")
glmm_pseudocaranx_dentex <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pseudocaranx_dentex, family = Gamma(link="log"))
summary(glmm_pseudocaranx_dentex)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pseudocaranx_dentex
##
## AIC BIC logLik deviance df.resid
## -3844.6 -3823.3 1926.3 -3852.6 1523
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.08244 0.2871
## Number of obs: 1527, groups: Transmitter, 31
##
## Dispersion estimate for Gamma family (sigma^2): 0.0955
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.53944 0.05554 -27.717 < 2e-16 ***
## Spawnyes 0.07720 0.01681 4.592 4.4e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pseudocaranx_dentex$KUD50 ~ data_pseudocaranx_dentex$Spawn)
#################################################################################
data_sciaena_umbra1 <- subset(week_kuds, File == "Sciaena_umbra1")
glmm_sciaena_umbra1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra1, family = Gamma(link="log"))
summary(glmm_sciaena_umbra1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra1
##
## AIC BIC logLik deviance df.resid
## -889.6 -878.1 448.8 -897.6 125
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0109 0.1044
## Number of obs: 129, groups: Transmitter, 15
##
## Dispersion estimate for Gamma family (sigma^2): 0.00121
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.704016 0.028254 -60.31 <2e-16 ***
## Spawnyes -0.010727 0.009007 -1.19 0.234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra1$KUD50 ~ data_sciaena_umbra1$Spawn)
#################################################################################
data_sciaena_umbra2 <- subset(week_kuds, File == "Sciaena_umbra2")
glmm_sciaena_umbra2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra2, family = Gamma(link="log"))
summary(glmm_sciaena_umbra2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra2
##
## AIC BIC logLik deviance df.resid
## -33.4 -30.8 20.7 -41.4 10
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 1.673e-11 4.091e-06
## Number of obs: 14, groups: Transmitter, 1
##
## Dispersion estimate for Gamma family (sigma^2): 0.0396
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.16613 0.07522 -15.50 <2e-16 ***
## Spawnyes -0.20429 0.10638 -1.92 0.0548 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra2$KUD50 ~ data_sciaena_umbra2$Spawn)
#################################################################################
data_scorpaena_porcus <- subset(week_kuds, File == "Scorpaena_porcus")
glmm_scorpaena_porcus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_porcus, family = Gamma(link="log"))
summary(glmm_scorpaena_porcus)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_porcus
##
## AIC BIC logLik deviance df.resid
## -282.9 -275.1 145.4 -290.9 48
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0004771 0.02184
## Number of obs: 52, groups: Transmitter, 13
##
## Dispersion estimate for Gamma family (sigma^2): 0.00659
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.70114 0.01877 -90.63 <2e-16 ***
## Spawnyes -0.05518 0.02466 -2.24 0.0253 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_porcus$KUD50 ~ data_scorpaena_porcus$Spawn)
#################################################################################
data_scorpaena_scrofa1 <- subset(week_kuds, File == "Scorpaena_scrofa1")
glmm_scorpaena_scrofa1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa1, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa1
##
## AIC BIC logLik deviance df.resid
## -363.4 -355.2 185.7 -371.4 54
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.003692 0.06077
## Number of obs: 58, groups: Transmitter, 7
##
## Dispersion estimate for Gamma family (sigma^2): 0.00229
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.71056 0.02962 -57.75 <2e-16 ***
## Spawnyes -0.02598 0.01977 -1.31 0.189
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa1$KUD50 ~ data_scorpaena_scrofa1$Spawn)
#################################################################################
data_scorpaena_scrofa2 <- subset(week_kuds, File == "Scorpaena_scrofa2")
glmm_scorpaena_scrofa2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa2, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa2
##
## AIC BIC logLik deviance df.resid
## -1820.4 -1803.5 914.2 -1828.4 504
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02351 0.1533
## Number of obs: 508, groups: Transmitter, 11
##
## Dispersion estimate for Gamma family (sigma^2): 0.0371
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.74615 0.04910 -35.57 <2e-16 ***
## Spawnyes 0.16387 0.01782 9.20 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa2$KUD50 ~ data_scorpaena_scrofa2$Spawn)
#################################################################################
data_seriola_dumerili <- subset(week_kuds, File == "Seriola_dumerili")
glmm_seriola_dumerili <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_seriola_dumerili, family = Gamma(link="log"))
summary(glmm_seriola_dumerili)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_dumerili
##
## AIC BIC logLik deviance df.resid
## -169.4 -153.9 88.7 -177.4 352
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04089 0.2022
## Number of obs: 356, groups: Transmitter, 8
##
## Dispersion estimate for Gamma family (sigma^2): 0.202
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.02861 0.08266 -12.444 <2e-16 ***
## Spawnyes 0.42232 0.04948 8.535 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_dumerili$KUD50 ~ data_seriola_dumerili$Spawn)
#################################################################################
data_seriola_rivoliana <- subset(week_kuds, File == "Seriola_rivoliana")
glmm_seriola_rivoliana <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_seriola_rivoliana, family = Gamma(link="log"))
summary(glmm_seriola_rivoliana)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_rivoliana
##
## AIC BIC logLik deviance df.resid
## -9159.6 -9135.8 4583.8 -9167.6 2783
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.006496 0.0806
## Number of obs: 2787, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.0613
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.667018 0.021514 -77.49 < 2e-16 ***
## Spawnyes 0.028314 0.009618 2.94 0.00324 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_rivoliana$KUD50 ~ data_seriola_rivoliana$Spawn)
#################################################################################
data_serranus_atricauda <- subset(week_kuds, File == "Serranus_atricauda")
glmm_serranus_atricauda <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_serranus_atricauda, family = Gamma(link="log"))
summary(glmm_serranus_atricauda)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_atricauda
##
## AIC BIC logLik deviance df.resid
## -5402.6 -5384.7 2705.3 -5410.6 646
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 9.271e-05 0.009629
## Number of obs: 650, groups: Transmitter, 9
##
## Dispersion estimate for Gamma family (sigma^2): 0.000501
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.796127 0.003653 -491.7 <2e-16 ***
## Spawnyes -0.004663 0.001812 -2.6 0.0101 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_atricauda$KUD50 ~ data_serranus_atricauda$Spawn)
#################################################################################
data_serranus_scriba <- subset(week_kuds, File == "Serranus_scriba")
glmm_serranus_scriba <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_serranus_scriba, family = Gamma(link="log"))
summary(glmm_serranus_scriba)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_scriba
##
## AIC BIC logLik deviance df.resid
## -111.0 -105.8 59.5 -119.0 23
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02159 0.1469
## Number of obs: 27, groups: Transmitter, 10
##
## Dispersion estimate for Gamma family (sigma^2): 0.0111
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.72592 0.08615 -20.033 <2e-16 ***
## Spawnyes 0.06062 0.07423 0.817 0.414
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_scriba$KUD50 ~ data_serranus_scriba$Spawn)
#################################################################################
data_solea_senegalensis <- subset(week_kuds, File == "Solea_senegalensis")
glmm_solea_senegalensis <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_solea_senegalensis, family = Gamma(link="log"))
summary(glmm_solea_senegalensis)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_solea_senegalensis
##
## AIC BIC logLik deviance df.resid
## -794.3 -780.5 401.2 -802.3 233
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04342 0.2084
## Number of obs: 237, groups: Transmitter, 22
##
## Dispersion estimate for Gamma family (sigma^2): 0.0426
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.49352 0.05066 -29.481 <2e-16 ***
## Spawnyes -0.03657 0.03680 -0.994 0.32
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_solea_senegalensis$KUD50 ~ data_solea_senegalensis$Spawn)
#################################################################################
data_sparisoma_cretense <- subset(week_kuds, File == "Sparisoma_cretense")
glmm_sparisoma_cretense <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparisoma_cretense, family = Gamma(link="log"))
summary(glmm_sparisoma_cretense)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparisoma_cretense
##
## AIC BIC logLik deviance df.resid
## -3587.9 -3569.3 1797.9 -3595.9 765
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02709 0.1646
## Number of obs: 769, groups: Transmitter, 10
##
## Dispersion estimate for Gamma family (sigma^2): 0.0153
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.662623 0.052688 -31.556 <2e-16 ***
## Spawnyes 0.004549 0.009085 0.501 0.617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparisoma_cretense$KUD50 ~ data_sparisoma_cretense$Spawn)
#################################################################################
data_sparus_aurata1 <- subset(week_kuds, File == "Sparus_aurata1")
glmm_sparus_aurata1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparus_aurata1, family = Gamma(link="log"))
summary(glmm_sparus_aurata1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata1
##
## AIC BIC logLik deviance df.resid
## -854.6 -843.2 431.3 -862.6 123
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.001468 0.03831
## Number of obs: 127, groups: Transmitter, 7
##
## Dispersion estimate for Gamma family (sigma^2): 0.00199
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.768618 0.016448 -107.53 <2e-16 ***
## Spawnyes 0.003994 0.009427 0.42 0.672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata1$KUD50 ~ data_sparus_aurata1$Spawn)
#################################################################################
data_sparus_aurata2 <- subset(week_kuds, File == "Sparus_aurata2")
glmm_sparus_aurata2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparus_aurata2, family = Gamma(link="log"))
summary(glmm_sparus_aurata2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata2
##
## AIC BIC logLik deviance df.resid
## -2100.7 -2080.6 1054.3 -2108.7 1129
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.2786 0.5279
## Number of obs: 1133, groups: Transmitter, 43
##
## Dispersion estimate for Gamma family (sigma^2): 0.0996
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.01828 0.08383 -12.147 <2e-16 ***
## Spawnyes 0.05459 0.02412 2.264 0.0236 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata2$KUD50 ~ data_sparus_aurata2$Spawn)
#################################################################################
data_sphyraena_viridensis1 <- subset(week_kuds, File == "Sphyraena_viridensis1")
glmm_sphyraena_viridensis1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis1, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis1
##
## AIC BIC logLik deviance df.resid
## -4622.5 -4601.8 2315.2 -4630.5 1294
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.009475 0.09734
## Number of obs: 1298, groups: Transmitter, 13
##
## Dispersion estimate for Gamma family (sigma^2): 0.0478
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.68092 0.02922 -57.53 <2e-16 ***
## Spawnyes 0.02217 0.01240 1.79 0.0739 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis1$KUD50 ~ data_sphyraena_viridensis1$Spawn)
#################################################################################
data_sphyraena_viridensis2 <- subset(week_kuds, File == "Sphyraena_viridensis2")
glmm_sphyraena_viridensis2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis2, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis2
##
## AIC BIC logLik deviance df.resid
## -292.5 -275.2 150.2 -300.5 554
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.09615 0.3101
## Number of obs: 558, groups: Transmitter, 17
##
## Dispersion estimate for Gamma family (sigma^2): 0.219
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.06979 0.08232 -12.995 < 2e-16 ***
## Spawnyes 0.27259 0.04106 6.639 3.16e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis2$KUD50 ~ data_sphyraena_viridensis2$Spawn)
#################################################################################
data_spondyliosoma_cantharus <- subset(week_kuds, File == "Spondyliosoma_cantharus")
glmm_spondyliosoma_cantharus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_spondyliosoma_cantharus, family = Gamma(link="log"))
summary(glmm_spondyliosoma_cantharus)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_spondyliosoma_cantharus
##
## AIC BIC logLik deviance df.resid
## -1988.4 -1970.4 998.2 -1996.4 659
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06214 0.2493
## Number of obs: 663, groups: Transmitter, 21
##
## Dispersion estimate for Gamma family (sigma^2): 0.0586
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.46186 0.05876 -24.877 < 2e-16 ***
## Spawnyes -0.05414 0.02015 -2.687 0.00721 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_spondyliosoma_cantharus$KUD50 ~ data_spondyliosoma_cantharus$Spawn)
#################################################################################
#Spawn
lm_spawn <- lm(KUD95 ~ Spawn, data=week_kuds)
glm_spawn <- glm(KUD95 ~ Spawn, data=week_kuds, family=Gamma(link="log"))
gam_spawn <- gam(KUD95 ~ Spawn, data=week_kuds, family=Gamma(link="log"))
glmmF_spawn <- glmmTMB(KUD95 ~ Spawn + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_spawn <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_spawn <- glmmTMB(KUD95 ~ Spawn + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_spawn <- gamm(KUD95 ~ Spawn, random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods
##
## Maximum number of PQL iterations: 20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_spawn <- gamm4(KUD95 ~ Spawn, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_spawn <- gamm(KUD95 ~ Spawn, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods
##
## Maximum number of PQL iterations: 20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
## iteration 6
## iteration 7
#gamm4T_spawn <- gamm4(KUD95 ~ Spawn, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_spawn <- gamm(KUD95 ~ Spawn, random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods
##
## Maximum number of PQL iterations: 20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
#gamm4S_spawn <- gamm4(KUD95 ~ Spawn, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_spawn, glm_spawn, gam_spawn, glmmF_spawn, glmmT_spawn, glmmS_spawn)
## df AIC
## lm_spawn 3 60419.37
## glm_spawn 3 35381.60
## gam_spawn 3 42803.78
## glmmF_spawn 4 20393.15
## glmmT_spawn 4 10135.23
## glmmS_spawn 4 25459.97
summary(gammF_spawn$lme)
## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30561.22 30593.82 -15276.61
##
## Random effects:
## Formula: ~1 | File
## (Intercept) Residual
## StdDev: 0.3692995 0.4372845
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.08808436 0.0542129 25563 1.624786 0.1042
## XSpawnyes 0.04672129 0.0058480 25563 7.989281 0.0000
## Correlation:
## X(Int)
## XSpawnyes -0.056
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7294488 -0.5107414 -0.2007781 0.1655619 18.8074454
##
## Number of Observations: 25612
## Number of Groups: 48
summary(gammT_spawn$lme)
## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 16203.53 16236.13 -8097.763
##
## Random effects:
## Formula: ~1 | Transmitter
## (Intercept) Residual
## StdDev: 0.3813896 0.3148426
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.06814541 0.013832668 24761 4.926411 0
## XSpawnyes 0.05285775 0.004361867 24761 12.118149 0
## Correlation:
## X(Int)
## XSpawnyes -0.17
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.4112885 -0.4456240 -0.1179616 0.1086597 16.9319189
##
## Number of Observations: 25612
## Number of Groups: 850
summary(gammS_spawn$lme)
## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 38606.74 38639.35 -19299.37
##
## Random effects:
## Formula: ~1 | Species
## (Intercept) Residual
## StdDev: 0.3844374 0.5125622
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.12112127 0.07162379 25581 1.691076 0.0908
## XSpawnyes 0.06544614 0.00677943 25581 9.653640 0.0000
## Correlation:
## X(Int)
## XSpawnyes -0.049
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.4817810 -0.4586271 -0.2083884 0.1350119 17.0458995
##
## Number of Observations: 25612
## Number of Groups: 30
#Spawn
lm_spawn1 <- lm(KUD50 ~ Spawn, data=week_kuds)
glm_spawn1 <- glm(KUD50 ~ Spawn, data=week_kuds, family=Gamma(link="log"))
gam_spawn1 <- gam(KUD50 ~ Spawn, data=week_kuds, family=Gamma(link="log"))
glmmF_spawn1 <- glmmTMB(KUD50 ~ Spawn + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_spawn1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_spawn1 <- glmmTMB(KUD50 ~ Spawn + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_spawn1 <- gamm(KUD50 ~ Spawn, random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods
##
## Maximum number of PQL iterations: 20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_spawn1 <- gamm4(KUD50 ~ Spawn, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_spawn1 <- gamm(KUD50 ~ Spawn, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods
##
## Maximum number of PQL iterations: 20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4T_spawn1 <- gamm4(KUD50 ~ Spawn, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_spawn1 <- gamm(KUD50 ~ Spawn, random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods
##
## Maximum number of PQL iterations: 20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_spawn1 <- gamm4(KUD50 ~ Spawn, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_spawn1, glm_spawn1, gam_spawn1, glmmF_spawn1, glmmT_spawn1, glmmS_spawn1)
## df AIC
## lm_spawn1 3 -31827.02
## glm_spawn1 3 -52600.77
## gam_spawn1 3 -47250.51
## glmmF_spawn1 4 -65918.50
## glmmT_spawn1 4 -76088.67
## glmmS_spawn1 4 -61271.32
summary(gammF_spawn1$lme)
## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 23069.61 23102.22 -11530.81
##
## Random effects:
## Formula: ~1 | File
## (Intercept) Residual
## StdDev: 0.3168224 0.3777924
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.5028836 0.04651849 25563 -32.30723 0
## XSpawnyes 0.0399475 0.00505236 25563 7.90671 0
## Correlation:
## X(Int)
## XSpawnyes -0.056
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7968283 -0.4771821 -0.2368402 0.1254108 25.1725237
##
## Number of Observations: 25612
## Number of Groups: 48
summary(gammT_spawn1$lme)
## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 10048.81 10081.42 -5020.407
##
## Random effects:
## Formula: ~1 | Transmitter
## (Intercept) Residual
## StdDev: 0.335805 0.2792604
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.5089937 0.012187944 24761 -123.81036 0
## XSpawnyes 0.0438886 0.003868605 24761 11.34481 0
## Correlation:
## X(Int)
## XSpawnyes -0.171
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.57124862 -0.40877404 -0.12324516 0.09659081 17.11076084
##
## Number of Observations: 25612
## Number of Groups: 850
summary(gammS_spawn1$lme)
## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30096.7 30129.3 -15044.35
##
## Random effects:
## Formula: ~1 | Species
## (Intercept) Residual
## StdDev: 0.3326315 0.4340953
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4739659 0.06193078 25581 -23.800214 0
## XSpawnyes 0.0499525 0.00574165 25581 8.700025 0
## Correlation:
## X(Int)
## XSpawnyes -0.048
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.5690784 -0.4951357 -0.2216829 0.1296522 23.1223745
##
## Number of Observations: 25612
## Number of Groups: 30
glmmT_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmF_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Species), data=week_kuds, family=Gamma(link="log"))
glmmTF_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmTS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log"))
glmmFS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|File) + (1|Species), data=week_kuds, family=Gamma(link="log"))
glmmTFS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File) + (1|Species), data=week_kuds, family=Gamma(link="log"))
AIC(glmmT_total, glmmF_total, glmmS_total, glmmTF_total, glmmTS_total, glmmFS_total, glmmTFS_total)
## df AIC
## glmmT_total 16 9783.083
## glmmF_total 16 20264.812
## glmmS_total 16 22364.422
## glmmTF_total 17 9638.072
## glmmTS_total 17 9727.926
## glmmFS_total 17 20266.812
## glmmTFS_total 18 9640.072
#Now we need to compare if the models are statistically different or not
anova(glmmT_total, glmmTF_total) #significant differences, this two models differ statistically
## Data: week_kuds
## Models:
## glmmT_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmT_total: Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmT_total: MonitArea_km2 + (1 | Transmitter), zi=~0, disp=~1
## glmmTF_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total: Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total: MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmT_total 16 9783.1 9913.5 -4875.5 9751.1
## glmmTF_total 17 9638.1 9776.6 -4802.0 9604.1 147.01 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(glmmT_total, glmmTFS_total) #significant differences, this two models differ statistically
## Data: week_kuds
## Models:
## glmmT_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmT_total: Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmT_total: MonitArea_km2 + (1 | Transmitter), zi=~0, disp=~1
## glmmTFS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total: Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total: MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmT_total 16 9783.1 9913.5 -4875.5 9751.1
## glmmTFS_total 18 9640.1 9786.8 -4802.0 9604.1 147.01 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(glmmTF_total, glmmTFS_total) #non significant differences, this two models do not differ statistically
## Data: week_kuds
## Models:
## glmmTF_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total: Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total: MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## glmmTFS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total: Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total: MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTF_total 17 9638.1 9776.6 -4802 9604.1
## glmmTFS_total 18 9640.1 9786.8 -4802 9604.1 0 1 1
anova(glmmTF_total, glmmTS_total) #non significant differences, this two models do not differ statistically
## Data: week_kuds
## Models:
## glmmTF_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total: Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total: MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## glmmTS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTS_total: Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTS_total: MonitArea_km2 + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTF_total 17 9638.1 9776.6 -4802 9604.1
## glmmTS_total 17 9727.9 9866.5 -4847 9693.9 0 0 1
anova(glmmTS_total, glmmTFS_total) #significant differences, this two models differ statistically
## Data: week_kuds
## Models:
## glmmTS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTS_total: Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTS_total: MonitArea_km2 + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## glmmTFS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total: Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total: MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTS_total 17 9727.9 9866.5 -4847 9693.9
## glmmTFS_total 18 9640.1 9786.8 -4802 9604.1 89.853 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#choose the simplest model with the lower AIC, that is statistically different from the others, which means the model with Transmitter and File as random effect (glmmTF_total)
#Backward elimination KUD95
Total1 <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1)
## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability +
## Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity +
## MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9727.9 9866.5 -4847.0 9693.9 25595
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.08341 0.2888
## Species (Intercept) 0.02107 0.1452
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0746
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1642530 0.3612144 -0.455 0.64931
## LengthStd 0.2888442 0.1570213 1.840 0.06584 .
## BodyMassStd -0.0658556 0.1148029 -0.574 0.56621
## Longevity -0.0019039 0.0035062 -0.543 0.58712
## Vulnerability -0.0052428 0.0042805 -1.225 0.22065
## Troph 0.0690349 0.1103588 0.626 0.53161
## Habitatdemersal -0.0869273 0.1010824 -0.860 0.38981
## Habitatpelagic-neritic 0.4101459 0.1583945 2.589 0.00961 **
## Migrationoceanodromous 0.1165872 0.1269472 0.918 0.35841
## ComImportmedium -0.1628333 0.0729114 -2.233 0.02553 *
## ComImportminor -0.2017707 0.1157973 -1.742 0.08143 .
## Spawnyes 0.0565985 0.0038023 14.885 < 2e-16 ***
## ReceiverDensity 0.0003943 0.0006242 0.632 0.52753
## MonitArea_km2 0.0293867 0.0036892 7.966 1.64e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total2 <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total2)
## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + BodyMassStd + Vulnerability + Troph + Habitat +
## Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 +
## (1 | Transmitter) + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9726.2 9856.6 -4847.1 9694.2 25596
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.08341 0.2888
## Species (Intercept) 0.02140 0.1463
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0746
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1214068 0.3544989 -0.342 0.7320
## LengthStd 0.2927254 0.1571009 1.863 0.0624 .
## BodyMassStd -0.0655384 0.1151142 -0.569 0.5691
## Vulnerability -0.0061194 0.0039752 -1.539 0.1237
## Troph 0.0594497 0.1096572 0.542 0.5877
## Habitatdemersal -0.0883555 0.1015518 -0.870 0.3843
## Habitatpelagic-neritic 0.4509230 0.1411373 3.195 0.0014 **
## Migrationoceanodromous 0.1104295 0.1272993 0.867 0.3857
## ComImportmedium -0.1636451 0.0733427 -2.231 0.0257 *
## ComImportminor -0.2003437 0.1163942 -1.721 0.0852 .
## Spawnyes 0.0566086 0.0038023 14.888 < 2e-16 ***
## ReceiverDensity 0.0003934 0.0006253 0.629 0.5293
## MonitArea_km2 0.0292097 0.0036805 7.936 2.08e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total3 <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Vulnerability + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total3)
## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + BodyMassStd + Vulnerability + Habitat + Migration +
## ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1 |
## Transmitter) + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9724.5 9846.8 -4847.3 9694.5 25597
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.08334 0.2887
## Species (Intercept) 0.02199 0.1483
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0746
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0355433 0.2044767 0.174 0.8620
## LengthStd 0.2902111 0.1573619 1.844 0.0652 .
## BodyMassStd -0.0670915 0.1154620 -0.581 0.5612
## Vulnerability -0.0048255 0.0032259 -1.496 0.1347
## Habitatdemersal -0.1015890 0.0995876 -1.020 0.3077
## Habitatpelagic-neritic 0.4939074 0.1187273 4.160 3.18e-05 ***
## Migrationoceanodromous 0.0890390 0.1224667 0.727 0.4672
## ComImportmedium -0.1615100 0.0740094 -2.182 0.0291 *
## ComImportminor -0.1821537 0.1125278 -1.619 0.1055
## Spawnyes 0.0566205 0.0038022 14.891 < 2e-16 ***
## ReceiverDensity 0.0004378 0.0006212 0.705 0.4810
## MonitArea_km2 0.0294025 0.0036688 8.014 1.11e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total4 <- glmmTMB(KUD95 ~ LengthStd + Vulnerability + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total4)
## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + Vulnerability + Habitat + Migration + ComImport +
## Spawn + ReceiverDensity + MonitArea_km2 + (1 | Transmitter) +
## (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9722.8 9837.0 -4847.4 9694.8 25598
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.08333 0.2887
## Species (Intercept) 0.02270 0.1507
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0746
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0305347 0.2064582 0.148 0.8824
## LengthStd 0.2237949 0.1078115 2.076 0.0379 *
## Vulnerability -0.0045419 0.0032282 -1.407 0.1594
## Habitatdemersal -0.0904350 0.0988607 -0.915 0.3603
## Habitatpelagic-neritic 0.4987129 0.1199140 4.159 3.2e-05 ***
## Migrationoceanodromous 0.0927178 0.1239680 0.748 0.4545
## ComImportmedium -0.1679172 0.0741210 -2.265 0.0235 *
## ComImportminor -0.1920240 0.1124932 -1.707 0.0878 .
## Spawnyes 0.0566271 0.0038022 14.893 < 2e-16 ***
## ReceiverDensity 0.0004720 0.0006207 0.760 0.4470
## MonitArea_km2 0.0297812 0.0036199 8.227 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total5 <- glmmTMB(KUD95 ~ LengthStd + Vulnerability + Habitat + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total5)
## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + Vulnerability + Habitat + ComImport + Spawn +
## ReceiverDensity + MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9721.4 9827.4 -4847.7 9695.4 25599
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.08331 0.2886
## Species (Intercept) 0.02366 0.1538
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0746
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0252828 0.2094189 0.121 0.9039
## LengthStd 0.2193664 0.1079018 2.033 0.0421 *
## Vulnerability -0.0036527 0.0030565 -1.195 0.2321
## Habitatdemersal -0.1325001 0.0828190 -1.600 0.1096
## Habitatpelagic-neritic 0.5402646 0.1086736 4.971 6.65e-07 ***
## ComImportmedium -0.1670076 0.0753550 -2.216 0.0267 *
## ComImportminor -0.1810782 0.1132225 -1.599 0.1098
## Spawnyes 0.0566394 0.0038022 14.896 < 2e-16 ***
## ReceiverDensity 0.0004970 0.0006228 0.798 0.4249
## MonitArea_km2 0.0297754 0.0036326 8.197 2.47e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total6 <- glmmTMB(KUD95 ~ LengthStd + Vulnerability + Habitat + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total6)
## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + Vulnerability + Habitat + ComImport + Spawn +
## MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9720.0 9817.8 -4848.0 9696.0 25600
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.08345 0.2889
## Species (Intercept) 0.02377 0.1542
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0746
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.046566 0.207978 0.224 0.8228
## LengthStd 0.214935 0.107669 1.996 0.0459 *
## Vulnerability -0.003580 0.003061 -1.170 0.2422
## Habitatdemersal -0.136720 0.082819 -1.651 0.0988 .
## Habitatpelagic-neritic 0.530651 0.108185 4.905 9.34e-07 ***
## ComImportmedium -0.162080 0.075258 -2.154 0.0313 *
## ComImportminor -0.174222 0.113121 -1.540 0.1235
## Spawnyes 0.056638 0.003802 14.896 < 2e-16 ***
## MonitArea_km2 0.027974 0.002851 9.811 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total7 <- glmmTMB(KUD95 ~ LengthStd + Habitat + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total7)
## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + Habitat + ComImport + Spawn + MonitArea_km2 +
## (1 | Transmitter) + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9719.3 9809.0 -4848.7 9697.3 25601
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.08333 0.2887
## Species (Intercept) 0.02589 0.1609
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0746
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.177114 0.084316 -2.101 0.0357 *
## LengthStd 0.214186 0.108057 1.982 0.0475 *
## Habitatdemersal -0.118887 0.083962 -1.416 0.1568
## Habitatpelagic-neritic 0.513836 0.110339 4.657 3.21e-06 ***
## ComImportmedium -0.161639 0.077914 -2.075 0.0380 *
## ComImportminor -0.113230 0.103585 -1.093 0.2743
## Spawnyes 0.056620 0.003802 14.891 < 2e-16 ***
## MonitArea_km2 0.028508 0.002827 10.085 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Test residuals for the best model (goodness of fit)
testDispersion(Total7) #plot with normality, dispersion and outliers
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.5858, p-value < 2.2e-16
## alternative hypothesis: two.sided
simulationOutput <- simulateResiduals(fittedModel = Total7, plot = F) #dispersion test
testDispersion(simulationOutput) #dispersion test
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.5858, p-value < 2.2e-16
## alternative hypothesis: two.sided
plot(simulationOutput) #residual analysis
plotQQunif(simulationOutput) #Q-Q plot (normality checking)
plotResiduals(simulationOutput) #residual vs. predicted (homoscedasticity checking)
testOutliers(simulationOutput) #outliers checking
##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: simulationOutput
## outliers at both margin(s) = 253, observations = 25612, p-value =
## 0.0008353
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
## 0.008703342 0.011166137
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 )
## 0.009878182
#Simulations from the model
getObservedResponse(Total7) #response used to fit the model
## [1] 1.079 0.839 3.494 0.819 1.162 0.764 0.893 1.385 1.284 1.772
## [11] 1.433 1.812 2.506 1.990 0.762 0.762 0.879 0.990 1.074 1.063
## [21] 1.078 1.020 1.103 0.909 1.131 0.865 0.864 0.874 0.898 0.873
## [31] 1.736 1.943 1.911 1.812 1.923 1.906 1.931 1.969 1.818 1.800
## [41] 1.884 1.790 1.642 1.577 1.780 1.762 1.762 1.803 1.782 1.871
## [51] 1.819 0.929 1.496 1.629 1.738 1.714 1.779 1.851 1.842 1.362
## [61] 1.697 1.587 1.923 1.977 1.733 2.198 2.102 2.254 2.059 1.993
## [71] 1.755 1.862 1.833 1.751 1.797 1.726 1.699 1.685 1.774 1.814
## [81] 1.914 1.724 1.840 1.799 0.856 1.668 1.138 1.003 0.902 0.960
## [91] 0.912 0.840 0.899 0.903 0.913 0.916 1.003 0.931 0.836 0.840
## [101] 0.835 0.795 0.827 0.841 0.829 0.842 0.820 0.818 0.813 1.044
## [111] 1.117 1.126 1.121 1.096 1.077 1.160 1.012 1.020 1.716 0.983
## [121] 0.868 0.858 1.061 1.100 0.883 0.885 0.850 1.673 1.012 1.060
## [131] 0.874 0.862 0.998 1.788 1.553 1.132 1.659 1.383 0.919 0.835
## [141] 0.924 0.956 0.955 0.839 1.104 1.002 1.093 1.125 1.072 1.019
## [151] 1.011 1.138 1.078 1.072 1.099 1.083 0.962 1.107 1.078 1.113
## [161] 1.116 1.278 0.937 1.136 1.134 1.130 1.103 1.118 1.119 1.102
## [171] 1.121 0.933 0.855 1.259 1.454 1.069 0.912 0.786 0.997 1.621
## [181] 1.606 1.702 1.469 1.001 1.512 1.652 1.264 1.221 1.552 1.881
## [191] 0.928 1.161 0.967 1.043 1.138 0.951 1.273 0.818 0.893 0.816
## [201] 0.918 1.083 1.064 0.918 0.889 1.055 0.961 1.123 0.951 1.128
## [211] 1.111 1.037 1.106 1.119 1.101 0.949 1.427 1.653 1.950 1.734
## [221] 1.041 0.937 1.963 2.062 0.924 0.928 1.187 2.061 1.679 1.728
## [231] 1.763 1.623 1.945 2.114 1.274 1.029 1.531 1.430 1.611 0.938
## [241] 0.958 0.919 0.856 0.910 0.851 0.805 0.803 0.838 0.920 0.799
## [251] 0.781 0.782 0.936 0.878 1.037 1.020 1.141 1.177 1.373 1.298
## [261] 1.230 1.159 1.210 1.188 1.225 1.349 1.124 1.059 0.979 1.912
## [271] 1.478 1.047 0.982 1.227 1.516 1.508 1.334 1.612 1.280 1.173
## [281] 1.508 1.366 0.966 1.781 1.802 1.390 1.100 0.911 0.923 0.996
## [291] 0.818 1.075 0.946 0.980 0.959 1.040 1.117 1.061 1.005 1.113
## [301] 1.134 1.094 1.076 1.200 1.083 1.057 1.119 1.030 1.037 0.992
## [311] 1.013 1.051 1.055 1.023 0.938 0.959 1.049 1.120 1.107 1.138
## [321] 1.166 1.222 1.304 0.794 0.879 0.912 1.146 1.060 1.103 1.457
## [331] 1.487 1.597 1.793 1.560 1.959 1.751 1.593 1.651 1.028 1.648
## [341] 1.137 1.148 1.386 1.306 1.435 1.373 1.538 1.389 1.529 1.386
## [351] 1.502 1.803 1.854 1.887 1.829 2.110 2.254 1.646 1.921 2.071
## [361] 2.030 1.990 1.820 1.599 2.043 1.736 1.996 2.074 2.948 2.175
## [371] 1.843 2.186 1.510 1.825 1.847 1.512 1.349 1.623 0.984 0.933
## [381] 1.253 1.123 1.405 0.948 1.535 0.792 0.790 0.775 0.807 0.771
## [391] 0.782 0.776 0.769 0.771 0.791 1.307 1.012 0.868 1.220 1.172
## [401] 0.864 0.845 0.950 1.023 0.969 0.807 0.879 1.169 1.054 0.916
## [411] 1.289 1.037 1.305 1.052 1.356 1.685 1.732 1.323 1.282 1.253
## [421] 1.806 1.748 1.215 1.174 1.096 1.403 1.235 1.584 0.897 0.864
## [431] 0.912 0.829 0.848 0.824 0.803 0.798 0.807 0.970 0.940 0.817
## [441] 0.824 0.822 0.797 0.801 0.808 0.796 0.870 0.884 0.900 0.901
## [451] 0.816 0.904 0.937 0.940 1.833 1.672 0.977 0.979 1.051 1.554
## [461] 0.793 0.831 0.914 0.871 0.847 0.939 0.953 0.899 0.860 0.884
## [471] 0.908 0.915 0.867 0.836 1.064 0.850 0.863 0.810 0.980 0.870
## [481] 1.015 0.923 1.050 1.082 1.042 1.041 1.112 1.040 1.022 1.012
## [491] 0.961 1.032 0.898 0.785 0.829 1.058 1.049 0.905 0.788 0.789
## [501] 1.784 1.293 1.504 1.516 0.980 1.488 1.827 1.453 1.136 1.815
## [511] 1.667 1.423 1.564 0.882 1.024 1.042 1.078 0.792 0.812 1.198
## [521] 1.227 1.228 1.578 1.262 1.117 1.325 1.080 1.150 1.578 1.570
## [531] 1.509 1.192 1.720 1.520 1.606 1.407 1.252 1.264 1.392 1.353
## [541] 0.821 1.172 1.328 1.354 1.917 1.520 1.332 1.505 1.495 1.195
## [551] 1.459 1.257 1.298 1.074 1.277 1.266 1.222 1.120 1.471 1.298
## [561] 1.300 1.172 1.374 1.229 1.178 1.183 1.311 1.176 1.209 1.148
## [571] 1.181 1.648 0.820 0.800 0.801 0.788 0.797 0.800 0.808 0.809
## [581] 0.786 0.793 0.794 0.794 0.799 0.808 0.804 0.906 0.983 0.939
## [591] 0.895 0.893 0.819 0.795 0.834 0.791 0.795 0.785 0.800 0.893
## [601] 0.782 0.773 0.800 0.971 0.772 0.770 0.776 0.782 0.796 0.784
## [611] 0.861 0.767 0.823 0.771 0.783 0.800 0.802 0.924 0.803 0.799
## [621] 0.821 0.791 0.810 0.805 0.793 1.081 1.066 1.094 1.102 1.066
## [631] 1.132 1.074 1.092 1.121 1.156 1.111 1.114 1.083 1.143 1.102
## [641] 1.159 1.124 1.169 1.293 1.341 1.360 1.340 1.164 1.033 1.166
## [651] 1.230 1.358 1.294 1.302 1.567 1.338 1.318 1.270 1.243 1.183
## [661] 1.476 1.362 1.350 1.227 1.201 1.229 1.226 1.149 1.142 1.378
## [671] 0.842 1.247 1.120 1.142 1.088 1.089 0.825 0.794 0.827 0.794
## [681] 0.834 0.890 1.146 0.807 0.790 0.798 0.810 0.801 0.915 0.794
## [691] 0.793 0.826 0.823 0.799 0.849 1.262 1.639 0.784 1.391 1.830
## [701] 1.669 1.432 1.491 0.802 0.783 0.879 0.829 0.802 0.789 1.356
## [711] 1.359 1.358 1.275 1.311 1.316 1.341 1.710 1.299 1.196 1.537
## [721] 1.658 1.686 1.190 1.413 1.537 1.576 1.309 1.142 1.431 1.566
## [731] 1.650 1.246 0.983 1.213 1.288 1.420 1.486 1.516 1.163 1.411
## [741] 1.341 1.353 1.226 1.527 1.327 1.322 1.541 1.491 1.442 1.525
## [751] 1.271 1.068 1.128 1.629 1.797 1.642 1.285 1.459 1.786 1.494
## [761] 1.735 1.736 1.705 1.679 1.314 1.364 1.604 1.620 1.654 1.694
## [771] 1.752 1.825 1.719 1.522 1.848 1.710 1.457 1.441 1.630 1.880
## [781] 1.862 1.816 0.880 1.535 1.834 1.413 1.054 1.721 0.982 1.405
## [791] 0.824 0.988 1.009 1.137 1.448 1.327 1.469 1.413 1.212 1.428
## [801] 1.462 1.422 1.252 1.300 1.519 1.468 1.070 0.948 0.926 1.033
## [811] 1.079 0.955 0.840 0.843 0.875 0.796 1.380 0.939 0.766 0.764
## [821] 1.330 1.037 1.384 1.940 2.013 1.703 1.880 2.046 2.011 1.764
## [831] 1.500 1.890 1.996 1.501 1.546 2.650 1.915 1.464 1.350 1.783
## [841] 1.422 1.911 1.807 1.402 1.183 1.336 1.625 1.705 1.199 1.687
## [851] 2.112 1.626 1.959 1.773 1.752 2.016 1.840 1.673 1.341 1.148
## [861] 1.394 1.083 0.857 0.858 2.583 1.746 1.788 1.391 1.767 1.625
## [871] 1.467 1.423 0.877 1.167 0.761 0.762 0.762 0.762 0.762 1.015
## [881] 1.123 1.147 1.307 1.277 1.235 1.200 1.286 1.199 1.448 1.339
## [891] 1.381 1.672 1.310 1.180 1.548 1.501 1.462 1.208 1.507 1.414
## [901] 1.419 1.495 1.280 1.463 0.762 0.762 0.762 0.761 0.761 0.765
## [911] 0.763 0.761 0.763 0.766 0.761 0.765 0.762 0.761 0.761 0.761
## [921] 0.762 0.761 0.761 0.761 0.792 0.761 2.018 1.115 0.847 0.965
## [931] 1.270 1.032 1.309 1.631 1.245 1.355 1.385 1.173 1.299 0.802
## [941] 0.769 1.195 0.936 0.764 0.771 0.762 0.764 0.762 0.761 0.761
## [951] 0.762 0.762 0.761 0.761 0.766 0.929 0.929 1.006 1.019 0.950
## [961] 1.620 1.587 1.661 1.777 1.652 1.582 0.867 1.094 1.653 2.291
## [971] 2.184 2.684 2.598 1.782 0.776 1.723 0.869 0.821 1.863 1.561
## [981] 1.742 1.348 1.415 1.623 1.732 1.820 1.722 1.850 2.007 1.863
## [991] 1.962 1.820 1.859 1.988 2.412 0.761 1.761 1.655 1.063 1.189
## [1001] 0.949 1.010 3.857 1.045 1.243 1.267 1.283 1.063 1.320 1.008
## [1011] 1.348 1.335 1.352 1.281 1.295 1.263 1.209 1.264 1.137 1.028
## [1021] 0.897 1.046 1.048 1.161 1.068 1.335 1.326 1.312 1.350 1.240
## [1031] 1.696 2.186 2.458 0.761 2.594 1.289 1.153 1.233 1.358 1.258
## [1041] 1.334 1.336 1.351 1.321 1.252 1.169 0.766 0.766 0.761 0.766
## [1051] 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.762 4.133 3.133
## [1061] 0.763 0.761 0.761 0.762 0.762 0.762 0.761 0.762 0.765 0.761
## [1071] 0.766 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761
## [1081] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [1091] 0.761 0.763 0.761 0.763 0.761 0.762 0.761 1.567 1.713 1.394
## [1101] 1.585 0.762 1.573 1.312 1.335 0.898 1.456 0.806 0.826 0.839
## [1111] 0.948 0.952 1.157 1.193 1.166 1.575 2.087 1.509 2.145 1.829
## [1121] 2.255 1.498 2.222 1.760 2.122 2.226 2.227 2.248 2.205 2.066
## [1131] 2.026 1.794 1.717 1.881 1.993 2.109 8.410 7.846 8.805 4.991
## [1141] 3.834 6.338 8.343 6.595 7.410 7.560 6.803 7.530 5.109 5.559
## [1151] 5.943 1.492 1.689 1.608 1.796 0.946 1.024 1.535 0.901 0.846
## [1161] 1.620 1.297 1.415 1.588 1.749 1.275 1.054 1.532 1.882 1.158
## [1171] 1.896 1.722 1.723 2.081 1.995 2.254 1.996 2.209 1.550 2.101
## [1181] 1.674 1.667 1.710 2.215 1.516 2.192 2.050 1.932 2.043 2.179
## [1191] 1.910 2.239 1.964 2.132 2.088 1.994 1.780 1.617 1.894 1.793
## [1201] 1.687 1.499 2.307 1.906 1.819 1.806 1.459 1.925 1.010 1.700
## [1211] 1.040 1.559 1.155 1.644 1.397 1.657 1.791 7.752 7.273 5.443
## [1221] 2.245 2.754 1.496 2.823 3.655 7.074 7.297 2.410 1.601 1.642
## [1231] 2.241 1.344 0.943 1.328 1.328 1.048 1.286 1.209 1.525 1.304
## [1241] 1.098 1.445 1.052 1.681 1.442 1.669 1.765 1.081 1.165 1.429
## [1251] 1.352 1.845 1.781 1.587 1.798 2.282 2.091 2.135 1.883 1.740
## [1261] 1.844 1.747 1.659 1.516 1.819 1.826 1.748 1.761 1.779 1.619
## [1271] 1.959 2.447 1.864 2.127 2.095 1.472 1.705 2.113 1.353 1.848
## [1281] 1.058 1.857 1.120 1.042 1.304 1.038 1.756 1.016 1.517 1.041
## [1291] 1.268 1.162 1.451 1.703 1.567 1.456 1.230 1.071 1.481 1.076
## [1301] 1.601 1.389 2.114 2.345 2.211 2.835 3.964 4.869 2.149 2.740
## [1311] 1.866 1.629 2.017 1.116 0.868 1.265 1.006 1.415 1.031 1.109
## [1321] 1.083 1.302 1.334 0.914 0.978 1.148 1.365 1.361 1.522 1.421
## [1331] 1.561 1.034 1.142 1.023 1.368 1.387 1.425 1.926 1.353 1.989
## [1341] 1.929 1.787 1.932 1.717 1.104 1.477 1.329 1.820 1.283 1.489
## [1351] 1.318 1.576 1.553 1.363 2.196 1.534 2.081 1.384 1.312 1.280
## [1361] 1.091 0.828 1.322 1.192 1.372 1.388 1.413 1.291 1.356 1.260
## [1371] 1.375 1.497 1.355 1.257 1.961 1.275 0.944 0.967 1.114 0.822
## [1381] 0.862 1.068 1.708 1.787 1.947 1.828 1.726 1.726 1.640 1.593
## [1391] 1.415 1.454 1.007 0.761 0.761 0.762 0.762 0.761 1.118 0.761
## [1401] 0.762 0.861 1.252 0.761 0.761 0.762 0.761 0.764 0.762 0.761
## [1411] 0.761 0.761 1.162 0.761 0.826 0.761 0.761 0.761 0.761 0.761
## [1421] 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.761 0.761 0.877
## [1431] 0.761 0.762 0.849 1.333 0.761 0.761 0.766 0.762 0.761 0.764
## [1441] 0.762 0.761 0.764 1.285 0.761 0.761 1.325 1.009 0.761 0.761
## [1451] 0.968 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.233 1.276
## [1461] 1.411 0.764 0.761 0.762 0.761 0.761 1.088 0.761 0.761 0.761
## [1471] 0.761 0.761 0.761 0.761 1.528 0.764 0.761 1.260 0.762 0.761
## [1481] 0.761 1.044 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [1491] 0.761 0.761 1.375 1.527 2.818 1.516 1.310 1.263 0.761 1.516
## [1501] 0.761 1.517 1.517 2.257 2.018 0.761 2.932 3.674 2.695 1.452
## [1511] 3.017 1.376 1.517 2.218 1.516 1.526 0.761 1.524 0.762 0.762
## [1521] 0.761 0.761 2.412 1.161 3.188 1.714 3.432 1.593 3.214 3.654
## [1531] 0.761 1.736 1.173 0.761 0.761 0.761 0.761 1.097 0.761 0.761
## [1541] 0.761 1.368 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [1551] 0.761 0.761 0.762 0.762 0.761 0.761 0.761 0.761 0.761 0.761
## [1561] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.762
## [1571] 0.761 0.762 0.761 0.761 0.761 0.762 1.513 1.194 1.468 0.762
## [1581] 0.761 0.761 0.761 0.761 0.761 0.761 1.245 1.516 0.761 0.761
## [1591] 0.766 0.762 0.761 0.901 0.761 0.761 0.761 0.762 0.761 1.526
## [1601] 0.889 1.431 0.762 0.761 0.761 0.761 0.761 0.761 0.955 1.345
## [1611] 1.516 0.761 0.761 0.766 0.762 0.761 0.895 0.761 0.761 0.761
## [1621] 0.761 1.414 1.554 1.725 1.558 1.086 1.195 0.913 1.082 0.762
## [1631] 0.761 0.762 0.761 0.764 0.761 0.761 0.984 1.414 0.926 0.945
## [1641] 1.113 1.126 1.047 1.345 1.488 1.717 1.516 1.104 1.183 1.065
## [1651] 1.007 0.890 0.904 0.764 0.762 0.761 0.761 1.023 0.761 0.761
## [1661] 0.955 1.333 1.140 0.991 1.042 1.104 0.995 0.761 0.761 0.761
## [1671] 0.761 1.524 1.809 1.516 1.148 0.766 0.762 0.761 1.091 0.766
## [1681] 0.762 0.761 1.496 1.439 1.296 1.521 1.411 1.632 1.517 2.391
## [1691] 1.276 1.257 1.387 0.761 0.762 2.068 2.110 0.763 3.779 2.213
## [1701] 2.763 1.842 0.766 1.200 1.928 2.254 0.763 3.663 3.694 3.016
## [1711] 2.175 0.766 0.761 0.761 0.761 0.761 0.764 0.761 0.761 0.761
## [1721] 1.287 3.733 1.411 2.219 2.174 2.984 2.957 1.926 3.514 2.182
## [1731] 2.023 3.849 1.464 1.485 3.615 3.768 2.181 1.453 2.203 1.745
## [1741] 1.340 3.541 3.713 3.418 2.157 2.634 2.261 2.330 1.978 0.761
## [1751] 0.761 0.764 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [1761] 0.761 0.761 0.761 0.761 0.761 0.764 0.761 0.761 0.761 0.761
## [1771] 0.761 0.761 0.761 1.376 0.763 0.761 0.761 0.761 0.764 0.761
## [1781] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [1791] 0.762 0.761 1.311 0.762 1.156 0.761 0.761 0.761 0.761 0.761
## [1801] 0.761 0.761 0.761 0.761 0.761 1.313 2.478 1.670 0.761 2.204
## [1811] 1.198 0.761 1.374 3.226 2.163 1.205 2.707 1.699 0.761 1.554
## [1821] 1.420 1.319 1.009 1.496 0.764 2.753 2.795 1.534 1.398 1.223
## [1831] 1.499 0.761 0.761 0.764 0.761 0.761 0.761 0.761 0.761 0.761
## [1841] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.763 0.761 0.761
## [1851] 0.761 0.764 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [1861] 0.761 0.761 0.761 0.761 0.970 1.171 1.482 0.766 0.762 0.761
## [1871] 1.041 1.723 1.078 0.762 0.761 1.201 1.062 1.032 1.822 1.892
## [1881] 1.728 1.765 1.019 0.903 0.839 1.651 1.841 1.628 1.715 1.411
## [1891] 1.475 2.052 1.378 3.841 1.072 1.325 1.257 1.011 1.306 1.258
## [1901] 0.761 0.761 0.883 1.036 0.761 0.761 0.836 1.083 1.742 1.213
## [1911] 1.743 1.731 0.761 0.761 0.761 0.761 0.761 0.761 1.593 1.217
## [1921] 0.761 0.761 1.482 1.697 1.357 1.497 1.525 1.694 1.636 1.483
## [1931] 0.761 1.517 1.411 1.704 1.729 1.224 1.716 1.476 0.761 1.517
## [1941] 1.342 1.725 1.694 1.668 1.742 1.466 2.171 2.124 0.761 1.501
## [1951] 1.011 0.761 0.761 1.411 1.562 1.387 0.761 0.761 1.513 1.652
## [1961] 1.475 1.103 0.761 0.761 1.453 1.639 1.572 0.927 0.761 1.517
## [1971] 0.761 1.400 1.493 0.761 1.310 1.480 0.761 0.761 1.454 1.370
## [1981] 0.764 0.762 0.761 0.761 1.388 1.442 0.927 1.297 1.494 0.761
## [1991] 0.761 1.445 1.381 0.764 0.762 2.346 1.919 1.771 1.753 1.679
## [2001] 1.620 1.666 1.674 1.723 1.672 1.742 1.753 1.691 1.645 1.695
## [2011] 1.670 0.761 0.761 0.761 1.514 0.761 1.186 0.761 0.761 0.761
## [2021] 0.761 0.761 0.761 1.535 0.761 0.761 0.761 0.761 1.517 0.761
## [2031] 1.139 0.761 0.761 0.761 0.761 0.761 0.761 1.532 0.761 1.496
## [2041] 0.761 0.761 0.761 0.761 0.761 0.760 0.761 0.761 0.761 0.761
## [2051] 0.764 0.762 1.502 0.761 0.761 0.761 0.761 0.761 0.760 0.761
## [2061] 0.761 0.761 0.761 0.764 0.762 1.152 1.737 1.647 2.019 1.461
## [2071] 0.761 1.161 1.739 1.632 1.615 1.400 0.761 0.761 0.760 1.825
## [2081] 0.832 0.760 0.760 0.760 0.761 0.761 0.760 0.761 0.760 1.803
## [2091] 0.762 0.760 0.760 0.760 0.761 0.761 0.760 0.761 0.761 0.761
## [2101] 0.761 0.761 0.761 0.761 0.761 1.326 0.972 0.894 0.893 1.219
## [2111] 0.790 1.423 1.146 0.914 0.907 0.849 0.768 1.473 1.463 1.339
## [2121] 1.477 1.499 1.475 1.524 1.508 1.523 1.447 1.353 1.165 1.469
## [2131] 1.313 1.528 1.412 1.406 1.457 1.354 1.477 1.476 1.457 1.519
## [2141] 1.495 1.523 1.461 1.386 1.213 1.457 1.339 1.526 1.877 1.490
## [2151] 2.716 1.813 0.762 0.761 0.761 4.662 0.783 1.149 0.774 1.073
## [2161] 0.812 1.844 2.007 0.968 0.814 1.411 0.874 1.950 1.488 0.839
## [2171] 2.564 1.493 1.143 0.761 2.128 1.809 1.855 1.208 0.911 1.260
## [2181] 1.691 0.822 1.260 1.256 2.799 0.985 0.762 0.761 4.898 0.870
## [2191] 0.761 1.116 1.427 1.120 2.081 2.024 0.915 0.822 1.383 1.029
## [2201] 1.862 1.338 0.793 2.253 1.220 1.032 0.761 2.233 1.458 1.218
## [2211] 1.133 0.769 1.284 1.510 0.909 1.527 1.386 1.524 1.526 1.296
## [2221] 1.526 1.339 1.457 1.513 0.761 1.363 1.487 1.413 1.535 1.521
## [2231] 1.474 1.527 1.368 1.526 1.524 1.324 1.525 1.358 1.456 1.516
## [2241] 0.761 1.369 1.470 1.453 1.531 1.527 1.474 0.763 5.602 0.906
## [2251] 0.762 1.994 1.996 0.849 1.949 3.564 3.282 0.761 1.803 0.763
## [2261] 0.860 3.306 2.570 2.866 3.495 1.590 3.759 1.740 0.897 3.517
## [2271] 3.326 2.138 2.861 1.548 0.761 3.365 4.136 2.711 0.808 0.761
## [2281] 0.761 0.762 1.819 4.482 2.947 2.276 1.314 3.196 1.902 1.644
## [2291] 4.300 1.381 1.976 2.350 2.811 1.401 1.313 2.580 2.143 2.310
## [2301] 2.124 3.354 1.304 2.123 1.903 2.123 2.701 1.344 0.761 2.731
## [2311] 2.893 0.761 1.584 0.761 5.206 2.251 4.528 3.570 1.529 2.272
## [2321] 2.751 2.619 1.178 4.502 2.775 4.483 5.689 2.514 7.286 1.729
## [2331] 1.455 1.374 2.030 2.073 2.116 1.751 1.455 1.527 0.761 0.802
## [2341] 3.374 1.154 2.435 1.619 3.440 0.762 0.762 4.270 4.847 3.389
## [2351] 2.024 3.062 0.762 2.459 0.764 2.213 0.762 3.317 1.918 4.377
## [2361] 1.743 6.218 1.832 3.935 3.502 4.627 2.368 4.496 4.090 4.301
## [2371] 2.478 0.762 0.767 1.712 2.395 0.871 2.035 2.095 0.761 1.839
## [2381] 1.440 1.066 1.679 0.761 0.761 0.763 2.505 1.345 0.761 0.761
## [2391] 0.762 0.762 0.761 0.761 0.761 0.761 0.906 0.761 0.761 1.237
## [2401] 0.763 1.703 0.817 0.761 0.761 0.761 1.300 0.761 0.761 0.761
## [2411] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [2421] 0.761 0.762 0.761 0.763 0.761 0.761 0.761 0.761 2.816 1.241
## [2431] 4.097 0.764 0.761 2.090 1.627 1.526 1.905 1.850 2.109 2.213
## [2441] 3.372 3.291 3.445 3.413 2.982 1.836 1.906 1.931 2.053 1.857
## [2451] 1.699 1.745 1.981 1.486 1.465 2.216 1.278 1.282 0.762 0.761
## [2461] 2.483 1.034 1.163 1.038 6.556 1.254 1.512 1.343 1.315 1.077
## [2471] 3.120 0.762 0.761 2.911 1.394 6.831 1.241 7.292 0.761 1.931
## [2481] 1.201 1.817 3.749 2.091 2.894 1.424 1.379 0.761 4.314 3.429
## [2491] 1.246 2.991 2.512 3.412 0.762 2.822 6.282 2.099 2.991 1.271
## [2501] 1.982 1.894 1.640 1.848 1.592 1.698 1.824 3.602 1.951 1.676
## [2511] 2.512 1.472 1.926 3.774 1.701 3.666 3.127 2.247 1.661 1.935
## [2521] 2.295 1.880 0.764 1.493 3.747 4.277 3.346 4.987 4.750 5.731
## [2531] 0.763 0.761 1.225 0.761 3.001 2.046 2.365 3.716 1.473 3.190
## [2541] 4.881 1.336 1.393 3.413 1.250 1.578 1.574 1.403 1.519 1.254
## [2551] 1.548 1.846 1.662 0.840 1.560 1.520 1.724 1.217 1.848 1.314
## [2561] 1.191 1.357 1.440 1.611 1.502 1.527 1.501 1.386 1.464 1.541
## [2571] 1.430 1.585 1.359 1.560 1.015 1.559 0.762 1.417 0.802 1.637
## [2581] 0.762 1.271 0.762 1.293 0.762 1.561 1.569 0.762 1.543 0.762
## [2591] 1.613 1.625 1.041 1.663 1.562 1.378 1.523 1.461 1.562 1.489
## [2601] 1.407 1.481 1.451 1.424 1.547 1.693 1.920 1.825 1.957 1.993
## [2611] 1.833 1.724 1.726 1.298 1.491 1.569 0.761 0.762 0.765 0.761
## [2621] 0.761 0.761 0.763 0.761 0.763 0.761 0.761 0.761 0.761 0.761
## [2631] 0.761 0.762 1.360 1.522 1.189 0.761 0.762 0.761 0.761 0.761
## [2641] 0.761 4.983 9.142 2.883 3.143 2.938 3.088 2.740 2.557 1.244
## [2651] 2.253 1.901 1.209 1.587 1.756 1.959 2.011 1.785 1.359 1.813
## [2661] 1.960 1.343 0.761 0.761 0.761 0.766 0.761 0.762 0.761 0.761
## [2671] 0.763 0.761 0.766 0.762 0.761 0.765 0.772 0.761 0.762 0.761
## [2681] 0.856 1.201 0.890 0.787 0.766 0.761 0.778 0.764 1.333 0.761
## [2691] 0.761 0.760 1.090 0.761 0.762 0.883 0.761 0.761 0.969 0.994
## [2701] 0.873 1.077 0.855 0.761 0.762 0.762 0.761 0.761 0.761 0.761
## [2711] 0.761 0.762 0.761 0.761 5.388 6.075 4.080 7.662 9.166 6.857
## [2721] 6.955 6.223 5.543 8.678 10.692 6.300 9.127 5.232 6.299 4.754
## [2731] 14.723 5.417 13.452 3.026 13.020 2.517 7.581 4.883 6.159 4.564
## [2741] 2.584 7.030 3.266 1.220 4.325 3.196 3.482 2.349 2.465 2.893
## [2751] 2.364 2.563 2.204 1.740 1.649 2.349 3.029 2.432 1.281 1.833
## [2761] 1.610 1.877 1.676 1.678 1.719 2.304 1.937 1.629 3.234 2.231
## [2771] 1.840 2.499 3.291 2.769 2.274 3.505 3.080 2.770 3.856 2.325
## [2781] 2.550 1.968 1.409 5.251 1.598 4.289 6.615 2.360 3.621 3.010
## [2791] 2.199 3.563 3.469 3.019 3.413 2.553 3.060 2.545 2.630 3.086
## [2801] 3.400 4.225 3.862 3.509 2.972 2.876 3.812 1.743 3.649 7.300
## [2811] 3.578 6.265 3.600 4.134 1.492 4.235 6.680 2.677 7.472 5.010
## [2821] 4.652 4.589 2.969 3.581 3.344 2.251 2.843 3.097 3.553 5.515
## [2831] 2.485 1.927 4.721 2.309 1.677 1.525 1.686 1.377 1.867 1.409
## [2841] 1.350 1.302 1.741 1.737 2.428 2.190 2.251 3.297 2.576 2.354
## [2851] 2.102 2.673 2.101 2.331 3.647 3.661 2.210 4.367 3.398 4.053
## [2861] 3.764 3.244 3.430 2.425 1.981 3.465 2.070 1.910 3.141 2.196
## [2871] 4.136 1.959 3.651 0.848 2.527 2.989 0.839 1.346 1.337 1.805
## [2881] 1.907 2.204 2.283 1.883 1.468 1.362 1.361 1.453 1.482 1.435
## [2891] 1.387 1.259 1.437 1.368 1.796 1.739 12.571 6.403 3.991 1.286
## [2901] 1.369 1.425 1.470 1.436 1.354 1.397 1.352 1.308 1.301 1.286
## [2911] 1.447 0.761 0.762 0.762 0.762 0.762 0.762 0.761 0.761 0.761
## [2921] 0.761 0.761 0.761 0.761 0.761 1.003 1.076 0.800 1.081 1.504
## [2931] 1.509 1.395 1.520 0.762 1.458 0.762 1.509 1.486 1.122 0.762
## [2941] 1.433 1.397 1.408 2.756 2.322 1.474 1.519 1.634 1.030 0.977
## [2951] 1.396 1.316 1.391 1.531 1.322 1.500 1.501 0.968 1.524 1.473
## [2961] 1.190 1.407 1.550 1.526 1.497 1.776 1.685 1.683 1.353 1.492
## [2971] 0.762 1.313 1.475 1.430 1.564 1.258 1.515 1.518 1.146 1.102
## [2981] 1.454 1.807 0.992 0.761 1.106 1.972 1.087 1.082 0.761 0.761
## [2991] 0.761 1.834 2.324 2.748 3.038 2.873 0.872 0.887 0.827 0.761
## [3001] 0.761 0.761 0.796 0.771 0.771 0.771 0.761 0.761 0.761 0.761
## [3011] 0.805 1.003 1.006 0.975 0.995 1.002 0.818 1.006 0.912 0.940
## [3021] 0.958 0.988 0.973 0.766 0.964 0.774 0.784 0.893 0.767 0.770
## [3031] 0.768 0.797 0.796 0.771 0.769 0.766 0.765 0.805 0.818 0.837
## [3041] 0.818 0.790 0.846 0.770 0.769 0.762 0.761 0.761 0.761 1.021
## [3051] 1.086 1.079 0.996 0.967 1.038 1.083 1.065 1.908 1.941 1.044
## [3061] 1.897 1.227 2.058 0.765 0.764 1.068 1.031 1.057 1.064 1.043
## [3071] 1.075 1.118 1.089 1.361 1.002 1.688 1.165 1.102 1.044 2.150
## [3081] 1.071 1.061 1.846 1.956 1.932 1.566 1.741 1.013 1.036 0.941
## [3091] 1.161 1.041 1.030 0.768 0.770 0.762 1.058 0.835 0.768 0.810
## [3101] 0.821 0.891 0.928 0.875 0.885 0.907 1.302 1.232 1.231 0.819
## [3111] 0.817 1.390 1.140 0.862 0.771 0.764 0.761 0.763 0.761 0.763
## [3121] 0.762 0.762 0.761 0.762 0.762 0.764 0.762 0.762 0.762 0.762
## [3131] 0.762 0.789 0.824 0.761 0.761 0.761 0.763 0.781 0.809 0.772
## [3141] 0.859 0.771 0.795 0.960 0.978 0.991 0.985 1.002 1.082 1.657
## [3151] 1.272 0.937 1.456 0.939 1.535 0.784 0.762 0.829 0.761 0.784
## [3161] 0.761 0.762 0.762 0.768 0.762 0.761 0.762 0.774 0.765 0.762
## [3171] 0.762 0.762 0.762 0.762 0.761 0.798 0.761 0.761 0.762 0.766
## [3181] 0.808 0.761 0.864 0.879 0.761 0.763 0.777 0.777 0.767 0.766
## [3191] 0.855 0.816 0.773 0.761 0.812 0.856 0.859 0.845 0.851 0.843
## [3201] 0.849 0.862 0.868 0.832 0.848 0.853 0.844 0.834 0.837 0.865
## [3211] 0.836 0.866 1.967 1.973 2.202 0.780 0.863 0.824 0.871 0.788
## [3221] 0.835 0.769 0.762 0.762 0.762 0.762 0.761 0.976 0.857 0.761
## [3231] 0.762 0.761 0.999 0.761 1.031 0.761 1.081 1.082 1.097 0.761
## [3241] 0.819 0.822 0.827 0.852 0.806 0.840 0.976 0.762 0.867 0.834
## [3251] 0.853 0.874 1.024 0.979 0.762 0.762 0.846 0.836 0.798 0.761
## [3261] 0.964 1.116 1.025 2.199 1.493 2.869 0.977 2.814 2.126 2.711
## [3271] 1.376 2.418 1.316 2.633 1.237 2.495 1.674 2.113 2.059 2.080
## [3281] 1.955 2.544 2.072 1.294 0.761 1.905 2.230 2.271 0.761 1.336
## [3291] 0.761 0.761 2.002 1.490 0.761 2.174 2.089 1.237 1.894 2.824
## [3301] 2.801 2.088 1.891 2.229 1.899 1.349 2.322 2.326 0.761 1.838
## [3311] 1.956 1.685 2.102 0.928 1.312 0.762 1.306 0.761 0.761 1.212
## [3321] 0.915 0.761 0.761 0.876 0.761 0.761 0.761 0.761 0.761 1.219
## [3331] 1.134 1.042 1.228 1.019 0.901 0.894 0.820 1.037 0.940 0.891
## [3341] 0.854 1.050 1.091 0.904 1.069 1.101 1.088 1.049 0.953 1.884
## [3351] 1.335 1.000 1.013 1.150 1.242 1.026 0.997 1.075 1.003 0.896
## [3361] 0.992 0.912 0.921 0.870 0.761 0.763 0.761 0.800 0.886 0.761
## [3371] 0.799 0.761 0.761 1.241 0.912 0.911 0.948 1.120 0.949 0.875
## [3381] 0.905 1.023 0.924 1.001 0.984 1.047 1.006 0.988 1.041 1.050
## [3391] 1.135 1.013 1.175 0.946 1.146 1.247 0.980 1.133 1.183 1.085
## [3401] 1.173 1.099 1.410 1.056 1.260 0.858 0.950 0.790 0.798 1.049
## [3411] 1.019 1.207 1.309 1.480 0.980 1.667 1.361 1.433 1.413 1.251
## [3421] 0.998 1.097 1.262 1.069 0.998 0.887 0.991 0.997 0.995 0.945
## [3431] 0.926 0.915 0.992 0.827 0.874 0.832 0.810 0.826 0.883 0.763
## [3441] 0.864 0.761 1.450 1.023 0.761 0.948 1.025 0.914 1.137 0.806
## [3451] 1.021 0.839 1.254 1.098 0.996 0.971 1.014 1.092 0.983 0.940
## [3461] 0.998 0.762 1.190 1.027 0.988 0.910 1.019 0.971 0.803 0.933
## [3471] 0.822 0.842 0.981 0.969 0.903 0.833 0.831 0.761 0.867 0.761
## [3481] 1.006 0.995 0.977 0.852 1.000 0.761 1.281 1.558 1.350 1.475
## [3491] 1.552 1.487 1.364 1.110 1.128 1.403 1.368 1.347 1.391 1.379
## [3501] 1.385 1.096 1.496 1.168 1.624 1.417 1.569 0.998 1.152 1.176
## [3511] 1.182 1.187 1.132 1.127 1.162 1.162 1.186 1.190 1.212 1.296
## [3521] 0.965 0.761 0.761 0.761 0.761 0.761 0.761 0.979 0.761 0.761
## [3531] 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3541] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3551] 0.762 1.191 0.762 0.762 0.762 1.167 0.762 0.762 0.762 0.762
## [3561] 1.367 0.762 0.762 0.762 1.579 0.762 0.762 1.054 0.762 0.762
## [3571] 0.921 0.762 1.021 0.762 0.762 0.762 0.762 1.387 0.762 0.762
## [3581] 0.762 1.347 0.762 0.762 0.762 0.762 0.762 1.122 0.762 0.762
## [3591] 0.762 1.710 0.762 0.762 1.389 0.762 0.762 0.762 0.823 0.762
## [3601] 0.762 0.762 0.762 0.762 0.762 0.818 0.762 0.762 0.762 0.762
## [3611] 0.762 0.762 0.762 0.762 1.217 0.762 0.762 0.762 0.823 0.762
## [3621] 0.762 0.762 1.115 0.762 0.762 0.762 0.762 0.994 0.762 0.762
## [3631] 0.813 0.762 0.762 0.762 0.904 0.762 0.762 0.762 0.762 0.762
## [3641] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3651] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3661] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3671] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3681] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3691] 0.762 0.762 0.762 0.762 1.269 1.088 0.841 0.762 0.762 0.762
## [3701] 1.048 0.762 0.870 0.762 0.762 0.762 1.201 0.762 1.065 0.762
## [3711] 1.168 0.762 1.398 0.762 1.237 0.762 1.243 0.762 0.762 1.250
## [3721] 0.762 0.762 1.049 0.762 0.762 0.762 1.015 0.762 0.762 0.898
## [3731] 0.762 0.762 0.909 0.762 0.762 0.839 0.762 0.762 0.762 0.875
## [3741] 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.762 0.762 0.762
## [3751] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3761] 0.762 0.762 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3771] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3781] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3791] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3801] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3811] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3821] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3831] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 1.069 1.046
## [3841] 1.024 0.996 1.251 0.937 0.849 0.846 0.823 0.850 0.897 0.786
## [3851] 0.761 0.761 0.784 0.861 0.926 0.947 0.876 0.837 1.058 1.076
## [3861] 1.055 0.763 0.815 0.793 0.813 0.774 0.778 0.787 0.904 0.867
## [3871] 0.765 0.761 0.761 0.761 1.055 1.213 1.157 1.077 0.925 1.103
## [3881] 1.200 1.528 1.117 1.146 1.160 1.244 1.241 1.039 1.174 1.189
## [3891] 1.148 1.300 1.053 1.273 1.110 1.084 0.942 0.867 0.983 1.132
## [3901] 0.939 1.214 0.913 0.785 0.761 0.761 0.847 0.761 0.762 0.804
## [3911] 0.761 0.761 0.761 0.761 0.980 0.894 1.038 1.397 1.320 1.755
## [3921] 1.596 1.341 1.113 0.908 0.775 0.869 1.031 1.008 0.979 0.938
## [3931] 0.988 1.460 1.273 1.432 0.834 1.103 1.103 1.197 1.263 1.242
## [3941] 1.249 1.286 1.328 1.201 1.215 1.127 1.257 1.286 1.291 1.292
## [3951] 1.358 1.288 1.236 1.160 1.146 1.107 0.997 0.813 1.386 1.368
## [3961] 1.265 1.267 1.201 1.137 1.140 1.282 0.858 0.965 1.057 1.173
## [3971] 0.969 0.838 0.888 0.788 0.761 0.761 0.761 0.761 0.908 0.761
## [3981] 0.761 1.139 0.917 1.380 0.761 0.761 1.127 1.033 0.761 0.777
## [3991] 0.768 0.785 0.768 0.765 0.766 0.776 0.786 0.816 0.766 0.767
## [4001] 0.768 0.801 0.798 0.762 0.762 0.889 0.915 0.770 0.792 0.875
## [4011] 0.761 0.788 0.834 0.859 0.846 0.846 0.861 0.873 0.822 0.803
## [4021] 0.814 0.812 0.900 0.857 0.866 0.859 0.968 0.994 0.924 0.871
## [4031] 0.779 0.859 0.806 0.788 0.834 0.859 0.846 0.846 0.861 0.873
## [4041] 0.822 0.803 0.814 0.812 0.900 0.837 0.786 0.984 0.886 0.886
## [4051] 0.761 0.761 0.761 0.761 0.764 0.762 0.761 0.761 0.761 0.761
## [4061] 0.761 0.761 0.774 0.764 1.244 1.205 1.125 0.781 0.762 0.796
## [4071] 0.793 0.925 1.013 0.795 0.801 0.819 0.995 0.860 0.877 0.919
## [4081] 1.029 0.794 1.037 1.117 1.013 0.803 0.845 0.827 0.838 0.772
## [4091] 0.810 0.936 0.790 0.872 0.996 0.814 0.813 0.794 0.902 0.847
## [4101] 0.787 0.786 0.852 0.838 0.848 0.833 0.802 0.771 0.838 0.838
## [4111] 0.829 0.774 0.835 0.763 0.806 0.855 1.181 0.829 0.814 0.762
## [4121] 0.934 0.761 1.029 0.981 0.935 0.863 0.932 0.924 0.988 0.984
## [4131] 0.965 1.154 0.980 0.928 0.999 1.024 1.076 0.987 1.015 1.040
## [4141] 1.028 1.003 0.991 0.904 1.013 1.066 1.088 1.050 1.056 1.031
## [4151] 1.025 1.069 1.103 1.048 1.015 1.041 1.087 1.020 1.023 1.053
## [4161] 1.034 1.010 1.040 1.071 1.031 1.024 1.053 1.081 1.047 1.021
## [4171] 1.025 1.044 1.006 1.247 0.960 1.055 1.043 1.014 1.406 1.326
## [4181] 1.210 0.980 0.766 0.767 0.863 1.646 1.436 1.583 1.686 1.592
## [4191] 1.670 1.557 0.996 1.154 1.594 1.058 0.982 1.234 1.266 1.194
## [4201] 1.177 1.183 1.046 1.023 0.988 0.990 1.046 0.957 0.989 1.010
## [4211] 0.991 0.963 0.998 1.007 0.968 1.007 1.456 1.445 1.319 1.332
## [4221] 1.187 0.925 1.058 1.052 1.080 1.171 1.051 1.169 1.153 0.927
## [4231] 0.833 0.826 0.824 0.815 0.879 0.850 0.801 0.772 0.796 0.850
## [4241] 0.831 0.851 0.796 0.823 0.899 0.852 0.812 0.808 0.808 0.802
## [4251] 0.842 0.898 0.857 0.892 0.819 0.882 0.902 0.921 0.917 1.115
## [4261] 1.029 1.018 1.007 1.001 0.980 1.049 1.560 1.107 1.106 0.989
## [4271] 1.134 1.038 1.103 1.107 0.953 0.954 1.076 1.086 1.058 1.033
## [4281] 0.924 1.014 1.080 1.069 1.043 1.072 1.040 1.067 1.093 1.096
## [4291] 1.115 1.083 1.037 1.191 1.010 1.077 1.025 1.033 1.052 1.028
## [4301] 1.066 0.974 0.894 0.887 0.962 0.927 0.899 0.990 0.998 1.158
## [4311] 1.067 1.071 1.079 1.056 1.167 1.211 1.199 1.157 1.527 1.341
## [4321] 1.106 1.128 1.106 1.162 1.045 1.103 1.148 1.063 1.019 1.015
## [4331] 1.055 0.993 1.063 0.967 0.882 1.073 1.081 0.985 1.061 1.105
## [4341] 0.931 0.885 0.927 0.796 0.818 0.761 0.839 1.098 0.809 0.882
## [4351] 0.986 1.077 0.884 1.047 1.050 1.081 1.059 1.016 1.145 1.133
## [4361] 1.114 1.087 0.978 1.170 1.238 1.091 1.208 1.016 1.130 1.018
## [4371] 0.971 1.047 1.063 1.296 1.092 1.094 1.545 1.038 1.149 1.190
## [4381] 1.045 1.108 0.969 1.070 1.100 1.322 1.512 0.892 1.067 1.078
## [4391] 1.064 1.079 1.094 1.052 1.104 1.109 1.084 1.140 1.099 1.148
## [4401] 1.044 0.987 1.097 1.069 1.077 1.096 1.047 1.019 1.080 1.058
## [4411] 1.078 1.082 1.023 1.031 1.053 1.183 1.149 0.984 1.074 0.895
## [4421] 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.836 0.848 0.772
## [4431] 0.860 0.761 0.761 0.761 0.761 0.762 0.761 0.766 0.929 1.050
## [4441] 0.973 1.012 1.053 0.982 1.059 1.048 1.060 1.127 1.090 1.065
## [4451] 1.064 1.090 1.097 1.021 1.024 1.034 1.032 1.059 1.044 0.986
## [4461] 1.149 1.024 1.010 1.068 0.988 0.936 0.988 0.792 0.761 0.869
## [4471] 0.761 0.861 1.139 0.859 1.046 1.087 1.145 0.858 0.853 0.897
## [4481] 1.020 0.957 0.916 0.981 1.155 0.796 0.989 0.966 1.036 0.843
## [4491] 0.962 0.972 1.063 0.951 0.931 0.853 0.864 0.762 0.817 0.909
## [4501] 0.864 0.761 0.761 0.761 0.889 0.888 0.961 0.969 0.900 0.793
## [4511] 0.846 0.862 0.874 0.954 0.918 1.068 1.041 1.148 1.345 1.063
## [4521] 0.848 1.036 1.027 0.875 0.801 0.860 0.935 0.868 0.844 0.915
## [4531] 0.830 0.924 0.917 1.071 1.279 1.024 1.001 1.043 0.959 0.895
## [4541] 0.914 1.015 0.875 0.838 0.845 0.903 1.054 0.921 0.888 0.828
## [4551] 0.820 0.886 0.987 0.899 1.043 0.772 0.873 0.922 1.187 1.262
## [4561] 1.119 1.111 1.219 1.180 1.238 1.118 0.964 1.424 1.199 1.609
## [4571] 1.287 1.686 1.551 1.183 1.272 1.668 1.772 1.436 1.214 0.902
## [4581] 1.156 1.285 1.212 1.153 1.173 1.165 1.238 1.248 1.243 1.012
## [4591] 1.162 1.520 1.334 1.367 1.422 1.459 1.367 1.183 1.174 1.421
## [4601] 1.459 1.431 1.332 1.287 1.358 1.246 1.203 1.209 1.156 1.328
## [4611] 1.589 1.239 1.267 1.214 1.102 1.113 1.051 0.997 1.037 0.990
## [4621] 0.992 0.910 1.244 1.001 1.131 1.097 1.320 1.221 1.062 0.847
## [4631] 0.857 0.987 1.010 0.883 0.861 1.040 0.874 0.950 0.921 0.984
## [4641] 0.918 0.893 0.878 1.020 1.040 0.970 0.915 0.895 0.941 0.995
## [4651] 1.089 0.901 0.855 1.047 0.852 1.102 0.906 0.941 0.809 1.107
## [4661] 0.942 0.911 0.891 1.142 1.168 1.016 1.168 0.938 0.891 1.024
## [4671] 0.997 0.932 1.026 1.108 1.040 0.987 1.059 0.961 1.032 1.077
## [4681] 1.435 1.133 1.027 1.300 0.976 1.050 0.856 1.023 0.985 1.040
## [4691] 0.962 0.948 0.974 0.953 0.881 0.970 0.839 0.886 0.958 1.061
## [4701] 0.852 1.106 0.962 0.963 1.044 1.167 1.026 1.008 1.012 0.981
## [4711] 0.892 0.978 0.993 1.042 0.937 1.020 1.124 0.973 1.120 1.161
## [4721] 1.096 1.220 1.240 1.151 1.155 1.106 1.112 1.147 1.193 1.049
## [4731] 1.108 1.075 1.191 1.507 1.224 1.147 2.007 1.434 1.293 1.184
## [4741] 1.056 1.288 1.262 1.347 1.283 1.238 1.436 1.322 1.411 1.314
## [4751] 1.403 1.287 1.365 1.217 1.267 1.280 1.228 1.246 1.221 1.188
## [4761] 1.112 1.226 1.164 1.147 1.261 1.202 1.274 1.024 1.315 1.323
## [4771] 1.068 1.184 1.212 0.921 1.335 1.237 1.118 0.805 1.308 0.961
## [4781] 0.926 1.184 1.042 1.354 1.167 1.566 1.378 1.115 0.801 1.003
## [4791] 0.959 1.386 0.761 1.213 1.049 1.182 1.319 1.146 1.021 0.966
## [4801] 0.930 1.153 1.134 1.029 1.175 1.002 1.174 0.940 0.998 0.941
## [4811] 1.023 1.112 1.323 1.396 1.276 1.137 1.284 1.234 0.982 1.172
## [4821] 0.993 1.538 1.308 1.135 1.296 1.140 1.310 1.285 1.288 1.261
## [4831] 1.276 1.143 1.255 1.148 1.347 1.452 1.678 1.690 1.529 1.638
## [4841] 1.278 1.379 1.415 1.137 1.877 1.131 1.432 1.386 1.427 1.363
## [4851] 1.466 1.417 1.354 1.036 1.049 1.543 1.525 1.533 1.519 1.563
## [4861] 1.546 1.562 1.467 1.570 0.997 1.253 1.199 1.115 1.159 1.209
## [4871] 1.179 1.210 1.211 1.188 1.396 1.212 0.849 0.765 0.766 0.762
## [4881] 0.768 0.779 0.779 0.785 0.774 0.776 0.794 0.819 0.858 0.840
## [4891] 0.792 0.787 0.829 0.957 0.768 0.787 0.774 0.811 0.803 0.774
## [4901] 0.784 0.767 0.762 0.762 0.913 0.824 0.816 0.786 0.844 0.812
## [4911] 0.817 0.796 0.785 0.828 0.785 0.819 0.764 0.866 0.879 1.027
## [4921] 0.973 1.048 0.989 1.017 1.053 0.986 1.017 1.004 1.050 1.035
## [4931] 1.050 0.985 1.016 1.103 1.020 0.998 1.024 0.985 1.133 1.063
## [4941] 0.868 1.014 0.945 0.951 1.014 1.069 0.999 1.050 1.071 1.154
## [4951] 1.108 1.040 1.004 1.040 0.775 1.002 1.019 1.047 1.055 1.030
## [4961] 1.068 1.055 1.013 1.045 0.942 1.042 1.063 0.970 1.056 1.042
## [4971] 1.029 1.033 0.798 0.892 1.004 1.004 1.045 1.056 1.112 1.058
## [4981] 1.090 1.098 1.124 1.117 0.827 0.934 1.089 1.096 1.107 1.084
## [4991] 1.058 1.068 1.041 1.079 1.061 1.138 1.051 1.130 1.142 1.309
## [5001] 0.969 1.321 1.844 1.522 0.827 1.031 1.068 1.282 0.810 0.796
## [5011] 0.777 0.801 1.748 0.795 0.771 0.806 0.777 0.771 0.787 0.775
## [5021] 0.795 0.801 0.775 0.777 0.775 0.773 0.791 0.799 1.064 0.761
## [5031] 0.761 0.761 0.767 0.900 0.925 0.943 0.762 0.762 0.762 0.762
## [5041] 0.762 0.762 0.762 0.766 0.766 0.763 0.761 0.761 0.761 0.763
## [5051] 0.848 0.761 0.761 0.761 0.761 0.761 0.773 0.761 0.761 0.761
## [5061] 0.761 0.761 0.761 0.827 0.761 0.761 0.797 0.793 0.812 0.807
## [5071] 0.846 0.761 0.761 0.761 0.761 0.819 0.765 0.860 0.762 0.762
## [5081] 0.765 0.762 0.766 0.765 0.776 0.770 0.851 0.796 1.031 1.309
## [5091] 0.789 0.952 0.912 0.851 0.761 0.873 0.824 0.781 0.761 0.761
## [5101] 0.761 0.761 0.761 0.761 0.761 0.849 0.811 0.791 0.788 0.825
## [5111] 0.808 0.825 0.807 1.097 0.761 0.761 0.761 1.038 1.413 0.852
## [5121] 0.892 0.837 0.762 0.829 0.762 0.763 0.783 0.772 0.784 0.804
## [5131] 0.791 0.761 0.858 0.932 1.026 0.761 1.356 0.766 0.761 0.761
## [5141] 0.761 0.761 0.761 0.761 0.761 0.761 0.865 0.828 0.801 0.824
## [5151] 0.811 0.850 0.823 1.087 1.137 0.761 1.149 1.172 1.081 1.424
## [5161] 0.964 0.784 0.770 0.774 0.771 0.770 0.819 0.765 0.773 0.773
## [5171] 0.770 0.777 0.790 0.764 0.776 0.768 0.764 0.855 0.785 0.766
## [5181] 0.765 0.767 0.802 0.792 0.780 0.801 0.788 0.772 0.774 0.762
## [5191] 0.768 0.762 0.875 0.771 0.791 0.762 0.762 0.857 0.772 0.809
## [5201] 0.781 0.799 0.781 0.762 0.769 0.778 0.776 0.771 0.766 0.763
## [5211] 0.768 0.773 0.769 0.770 0.765 0.779 0.788 0.762 0.770 0.850
## [5221] 0.962 0.763 0.762 0.762 0.762 0.904 0.765 0.767 0.763 0.761
## [5231] 0.773 0.763 0.763 0.771 0.872 0.769 0.814 0.808 0.799 0.796
## [5241] 0.902 0.810 0.794 0.786 0.803 0.788 0.789 0.811 0.816 0.813
## [5251] 0.808 0.814 0.821 0.827 0.808 0.810 0.777 0.839 0.805 0.791
## [5261] 0.797 0.790 0.769 0.762 0.762 0.762 0.829 0.801 0.772 0.806
## [5271] 0.786 0.776 0.780 0.782 0.766 0.819 0.779 0.773 0.768 0.787
## [5281] 0.837 1.310 0.868 0.786 0.784 0.762 0.765 0.762 0.762 0.794
## [5291] 0.761 0.761 0.761 0.761 1.292 0.761 1.032 1.113 1.209 1.051
## [5301] 0.761 0.761 0.865 0.761 0.761 0.761 0.761 0.761 0.761 0.791
## [5311] 0.798 0.797 0.813 0.773 0.775 0.815 1.117 0.761 0.761 0.973
## [5321] 1.116 0.979 0.837 0.761 0.765 0.793 0.773 0.769 0.783 0.782
## [5331] 0.772 0.781 0.799 0.779 0.819 0.856 0.846 1.170 0.805 0.952
## [5341] 0.873 1.444 0.820 0.785 0.805 0.797 0.807 0.822 0.817 0.838
## [5351] 0.793 0.841 0.837 0.853 0.792 0.873 0.859 0.853 0.893 0.872
## [5361] 0.875 0.847 0.823 0.840 0.877 0.902 2.365 2.365 0.819 0.793
## [5371] 0.766 0.776 0.761 0.765 0.769 0.809 0.761 0.761 0.761 0.761
## [5381] 0.762 0.766 1.064 0.978 1.035 0.981 1.072 0.956 1.010 0.979
## [5391] 0.885 0.765 0.783 0.798 0.762 0.761 0.809 0.856 0.863 0.761
## [5401] 0.910 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.824 0.864
## [5411] 0.865 0.810 1.147 0.993 1.097 0.761 1.116 1.157 0.761 0.761
## [5421] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [5431] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [5441] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.502 1.198
## [5451] 0.763 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.766
## [5461] 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.761 0.761
## [5471] 0.761 0.761 0.761 0.821 0.836 0.789 0.761 0.823 0.761 0.761
## [5481] 0.761 0.761 0.761 0.973 1.149 1.173 0.775 0.789 0.762 0.762
## [5491] 0.837 0.762 1.154 1.690 0.848 0.828 0.828 0.761 0.761 0.761
## [5501] 0.761 0.761 0.761 1.299 0.824 0.829 0.791 0.773 0.762 0.771
## [5511] 0.762 0.762 0.762 0.762 0.761 0.761 0.802 0.773 1.337 0.761
## [5521] 1.174 1.241 0.761 1.299 0.761 0.764 0.761 0.761 0.761 0.761
## [5531] 0.761 0.761 0.919 0.975 0.871 0.875 1.079 0.936 1.098 0.851
## [5541] 0.761 0.863 1.006 0.761 0.761 0.761 0.762 0.762 0.762 0.762
## [5551] 0.762 0.762 0.762 0.762 0.762 0.761 2.038 0.764 0.764 0.762
## [5561] 0.762 1.855 1.163 0.993 1.021 1.720 0.762 0.794 0.762 0.762
## [5571] 0.762 0.762 0.803 0.803 1.244 0.761 1.166 0.761 0.761 0.762
## [5581] 0.762 0.761 0.761 0.762 0.762 0.762 1.783 1.126 1.016 1.482
## [5591] 1.638 1.998 1.679 1.699 1.722 1.628 1.096 1.700 1.762 2.446
## [5601] 1.908 1.306 1.140 1.265 0.761 0.761 0.761 0.761 0.761 2.147
## [5611] 1.410 1.222 1.176 1.856 0.762 0.762 0.762 0.762 0.762 0.762
## [5621] 0.762 0.762 0.761 1.117 0.761 0.762 0.761 0.761 0.761 0.761
## [5631] 0.761 1.260 0.920 1.009 1.144 1.121 1.276 1.084 1.128 1.155
## [5641] 1.173 1.134 1.177 1.176 1.184 1.226 1.289 1.319 1.056 1.435
## [5651] 1.130 1.011 1.000 1.065 3.222 1.734 1.109 1.031 0.770 1.812
## [5661] 1.164 2.597 1.365 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [5671] 1.132 1.133 1.283 0.761 0.761 0.772 0.791 0.818 0.761 0.771
## [5681] 0.762 0.815 0.822 0.769 0.819 0.778 0.795 0.844 0.900 0.762
## [5691] 1.113 1.174 1.039 1.192 1.199 1.074 0.788 0.792 0.981 0.911
## [5701] 1.143 1.300 1.211 0.829 0.885 0.761 0.787 0.800 0.893 1.244
## [5711] 2.174 1.353 1.169 1.025 1.259 1.098 1.031 1.159 1.128 1.349
## [5721] 1.140 1.066 1.193 1.180 1.276 1.295 1.149 1.144 1.058 1.203
## [5731] 1.011 1.215 1.272 1.287 1.244 1.354 1.327 1.272 3.344 1.294
## [5741] 1.400 1.296 1.374 0.761 1.203 0.815 0.792 0.762 1.010 1.095
## [5751] 1.080 1.158 1.207 0.771 0.836 1.002 0.833 0.977 0.762 0.797
## [5761] 0.985 0.813 0.763 0.762 0.796 0.881 0.914 0.832 1.117 0.764
## [5771] 1.333 0.761 1.072 0.761 0.764 0.762 0.955 0.995 1.184 1.133
## [5781] 0.999 1.279 1.100 0.916 0.770 0.885 0.996 1.058 0.957 1.035
## [5791] 1.135 0.774 0.768 0.771 0.762 0.762 0.768 0.878 0.887 0.761
## [5801] 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.762 0.833
## [5811] 0.761 0.878 0.762 0.762 0.761 0.770 0.761 0.762 0.769 0.761
## [5821] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [5831] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.761
## [5841] 0.835 0.761 0.761 0.761 0.762 1.123 1.256 1.408 0.761 0.765
## [5851] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [5861] 0.761 0.761 0.764 0.761 0.995 1.062 0.762 0.895 0.761 0.761
## [5871] 0.761 0.871 0.761 1.631 1.184 1.121 1.075 1.267 3.512 0.761
## [5881] 0.761 0.761 0.761 0.761 0.761 0.804 0.761 0.761 0.814 0.813
## [5891] 0.761 0.761 0.979 0.761 0.761 1.631 0.761 0.897 0.762 0.761
## [5901] 2.297 1.059 0.762 1.158 0.761 0.761 1.074 1.253 1.264 1.245
## [5911] 1.204 0.832 1.157 1.120 1.103 0.987 0.906 1.059 1.066 0.884
## [5921] 0.999 1.054 0.783 0.786 0.782 1.187 1.268 0.786 0.766 1.231
## [5931] 0.762 0.768 0.761 0.761 0.761 1.292 1.327 1.145 1.052 1.152
## [5941] 1.124 1.108 1.051 1.598 1.568 1.727 1.552 1.684 0.761 0.761
## [5951] 1.596 1.775 0.761 1.452 1.707 1.493 1.488 1.192 0.761 0.761
## [5961] 0.762 0.762 0.761 0.761 0.762 0.762 1.220 1.941 1.927 0.761
## [5971] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761
## [5981] 0.761 0.761 0.761 0.761 2.037 1.246 1.844 0.761 0.762 0.762
## [5991] 0.761 0.761 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [6001] 0.762 0.762 0.761 1.952 0.761 0.762 1.549 2.321 0.761 0.761
## [6011] 0.761 0.761 0.762 0.762 0.761 0.761 0.761 0.762 0.762 0.762
## [6021] 0.762 0.762 0.762 0.762 0.762 0.762 0.829 1.580 1.279 1.138
## [6031] 1.264 1.212 2.022 1.286 0.761 0.761 0.761 0.761 0.761 0.761
## [6041] 0.766 0.761 0.763 0.761 0.761 1.311 1.303 1.136 0.761 0.761
## [6051] 0.761 0.761 0.761 1.769 1.898 2.127 2.362 1.746 0.761 0.761
## [6061] 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 1.080
## [6071] 0.761 1.912 0.761 1.374 0.761 0.762 0.998 1.139 1.195 1.115
## [6081] 1.054 1.153 1.185 1.287 1.313 0.992 1.091 0.892 0.986 1.160
## [6091] 1.321 1.109 1.398 1.187 0.787 0.813 1.029 1.066 0.920 1.114
## [6101] 1.127 1.381 1.267 0.761 0.792 0.761 0.761 0.799 0.761 0.761
## [6111] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [6121] 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 1.514 1.309
## [6131] 0.906 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.762 1.262
## [6141] 0.939 1.333 0.987 0.815 0.778 0.804 1.386 1.188 1.038 0.761
## [6151] 0.761 0.761 1.159 0.761 0.806 0.962 1.751 1.486 1.412 0.761
## [6161] 1.323 0.761 0.761 0.761 0.761 0.761 0.834 0.940 1.208 1.284
## [6171] 1.158 1.189 1.121 0.980 1.047 1.331 1.224 1.094 1.146 0.765
## [6181] 1.138 0.972 1.326 1.038 1.355 0.995 0.763 0.807 0.844 0.913
## [6191] 0.858 1.043 0.854 1.397 0.901 0.834 0.950 1.015 1.082 1.011
## [6201] 1.060 1.046 1.085 1.069 1.113 1.121 1.094 1.081 1.157 1.139
## [6211] 1.154 1.183 1.223 1.234 1.309 1.520 1.224 1.183 1.188 1.161
## [6221] 0.995 1.217 1.133 0.761 1.848 0.761 0.761 0.761 0.762 0.762
## [6231] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.761 1.272 0.761
## [6241] 1.029 1.755 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [6251] 0.762 0.762 1.132 0.762 0.761 0.762 1.311 0.947 2.638 0.988
## [6261] 1.070 1.137 1.105 1.219 1.216 1.253 1.216 1.185 1.213 1.204
## [6271] 1.174 1.306 1.985 1.381 1.128 1.069 1.393 1.078 1.155 1.163
## [6281] 1.211 1.290 1.075 1.181 1.015 0.764 1.167 0.886 0.887 0.762
## [6291] 0.993 1.118 0.981 1.197 1.138 3.118 0.764 0.761 0.761 2.782
## [6301] 1.025 1.056 0.796 0.776 0.802 0.764 0.932 0.799 0.762 0.838
## [6311] 0.883 0.777 0.762 0.957 1.108 0.806 0.762 0.762 0.762 0.771
## [6321] 0.761 0.762 0.762 0.762 0.862 0.761 1.288 0.764 0.761 0.761
## [6331] 0.761 0.761 1.008 0.920 1.210 0.851 0.857 0.909 1.065 1.033
## [6341] 0.955 0.882 0.988 1.050 0.820 0.964 0.785 1.280 0.764 1.042
## [6351] 0.761 1.113 0.928 0.801 0.881 0.974 1.001 0.761 0.761 0.761
## [6361] 0.762 0.762 0.981 1.206 1.098 1.144 0.955 0.947 0.841 0.820
## [6371] 2.620 1.826 2.352 1.249 1.329 1.486 0.806 0.774 1.133 1.003
## [6381] 1.054 1.309 1.126 1.153 1.066 1.104 1.246 1.070 1.134 0.761
## [6391] 0.767 0.775 1.952 0.900 1.174 1.090 0.833 0.784 0.888 0.858
## [6401] 0.862 1.002 1.044 1.111 1.149 1.074 1.033 1.024 1.174 1.329
## [6411] 1.107 0.804 1.001 1.372 1.191 1.369 1.181 1.150 1.179 1.188
## [6421] 1.082 1.179 1.027 0.999 1.214 1.217 1.348 2.056 0.769 1.511
## [6431] 0.761 0.762 0.761 0.761 0.761 0.761 1.618 0.961 0.906 1.163
## [6441] 1.148 1.183 1.302 1.029 1.174 1.209 1.316 1.224 1.196 1.118
## [6451] 1.208 1.281 0.911 0.968 1.353 0.762 0.899 1.001 1.003 1.044
## [6461] 0.935 0.899 0.944 0.802 2.113 1.383 1.134 1.131 1.217 1.227
## [6471] 1.155 1.212 1.011 1.178 1.196 0.761 1.213 1.215 1.237 1.130
## [6481] 0.824 0.963 1.156 1.012 0.800 0.811 1.129 1.245 0.877 0.958
## [6491] 1.008 1.130 3.690 0.933 0.765 1.435 1.226 2.928 1.320 0.813
## [6501] 0.852 0.899 0.761 0.762 0.814 0.805 0.827 0.798 0.955 0.837
## [6511] 1.860 1.398 0.937 0.874 1.348 0.982 0.761 0.769 0.786 0.761
## [6521] 0.761 0.761 0.761 0.762 0.760 0.901 0.762 0.761 0.761 0.761
## [6531] 0.761 0.761 0.761 0.761 0.853 0.943 0.947 1.190 1.863 3.208
## [6541] 1.336 0.920 1.005 3.019 0.761 0.788 0.766 0.943 1.014 0.761
## [6551] 0.762 0.762 0.760 1.100 0.761 1.164 1.103 1.140 1.080 1.036
## [6561] 0.818 0.762 0.780 0.809 0.789 0.818 0.965 0.762 2.057 1.061
## [6571] 0.892 1.023 1.267 0.992 0.957 0.966 0.892 0.966 1.178 1.036
## [6581] 1.273 1.156 1.029 1.007 1.225 1.943 1.217 0.762 0.762 0.762
## [6591] 0.762 0.762 0.762 0.761 0.762 0.761 0.762 0.762 0.762 0.762
## [6601] 0.762 1.221 0.761 1.404 0.761 0.762 0.762 0.762 0.762 0.851
## [6611] 1.024 0.762 1.211 0.782 0.762 1.767 1.202 1.076 1.201 1.061
## [6621] 0.761 1.113 3.092 1.218 1.142 1.483 0.761 0.761 0.761 0.761
## [6631] 0.761 0.761 0.761 0.943 0.761 1.759 0.866 1.112 0.761 0.816
## [6641] 0.761 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.762 0.762
## [6651] 0.762 0.762 0.762 0.764 0.762 1.478 0.762 0.761 0.762 0.762
## [6661] 0.762 0.762 0.762 0.762 0.761 0.764 2.367 0.761 0.873 0.762
## [6671] 1.313 1.866 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [6681] 0.764 0.762 0.760 0.762 0.761 0.764 0.787 0.796 0.761 0.864
## [6691] 1.248 1.009 1.163 0.831 1.703 1.238 0.761 0.762 0.962 1.368
## [6701] 1.177 1.950 1.052 0.762 0.924 0.932 0.846 0.762 0.897 0.762
## [6711] 0.905 0.761 0.761 0.762 0.762 0.995 0.761 0.761 0.761 0.761
## [6721] 0.761 0.902 0.761 0.761 0.761 0.777 0.761 1.447 1.470 0.761
## [6731] 0.761 0.761 1.857 0.762 1.585 0.775 0.761 0.761 0.765 0.762
## [6741] 0.762 0.761 0.763 0.765 0.762 0.762 0.762 0.762 0.765 0.765
## [6751] 0.762 0.762 0.762 0.765 0.765 0.762 0.762 0.761 0.766 0.765
## [6761] 0.762 0.762 0.761 0.765 0.765 0.762 0.762 0.762 0.765 0.761
## [6771] 0.762 0.762 0.761 0.765 0.765 0.762 0.762 0.762 0.761 0.765
## [6781] 0.765 0.762 0.762 0.762 0.766 0.785 0.825 0.762 0.762 0.761
## [6791] 0.765 0.791 0.762 0.762 0.761 0.766 0.870 0.762 0.762 0.761
## [6801] 0.765 0.765 0.762 0.762 0.761 0.765 0.765 0.762 0.765 0.808
## [6811] 0.765 0.765 0.762 0.935 0.761 0.765 0.761 0.762 0.762 0.761
## [6821] 0.765 0.765 0.762 0.762 0.761 0.765 0.765 0.762 0.761 0.761
## [6831] 0.765 0.765 0.762 0.762 0.761 0.765 0.762 0.762 0.761 0.765
## [6841] 0.762 0.761 0.761 0.765 0.762 0.762 0.761 0.765 0.762 0.761
## [6851] 0.761 0.765 0.762 0.762 0.761 0.765 0.762 0.762 0.761 0.765
## [6861] 0.762 0.762 0.761 0.765 0.762 0.761 0.766 0.765 0.762 0.762
## [6871] 0.765 0.761 0.765 0.762 0.762 0.765 0.762 0.765 0.765 0.762
## [6881] 0.765 0.765 0.762 0.765 0.765 0.762 0.761 0.765 0.761 0.765
## [6891] 0.762 0.762 0.765 0.761 0.787 0.762 0.786 0.765 0.761 0.765
## [6901] 0.762 0.762 0.765 0.761 0.765 0.762 0.761 0.765 0.761 0.765
## [6911] 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762 0.762 0.761
## [6921] 0.761 0.765 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762
## [6931] 0.765 0.761 0.761 0.765 0.762 0.762 0.761 0.761 0.761 0.765
## [6941] 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762 0.762 0.761
## [6951] 0.761 0.761 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762
## [6961] 0.762 0.761 0.761 0.765 0.762 0.762 0.762 0.761 0.762 0.765
## [6971] 0.762 0.762 0.762 0.761 0.761 0.763 0.762 0.762 0.761 0.761
## [6981] 0.761 0.765 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762
## [6991] 0.762 0.761 0.762 0.761 0.762 0.762 0.765 0.761 0.767 0.765
## [7001] 0.762 0.762 0.763 0.766 0.765 0.762 0.762 0.764 0.765 0.765
## [7011] 0.762 0.762 0.764 0.765 0.765 0.762 0.762 0.761 0.767 0.765
## [7021] 0.762 0.762 0.763 0.765 0.765 0.762 0.762 0.764 0.767 0.761
## [7031] 0.762 0.762 0.762 0.765 0.765 0.762 0.762 0.761 0.766 0.764
## [7041] 0.762 0.762 0.765 0.763 0.763 0.762 0.762 0.762 0.765 0.762
## [7051] 0.762 0.762 0.763 0.766 0.762 0.762 0.762 0.761 0.765 0.765
## [7061] 0.762 0.762 0.761 0.765 0.765 0.762 0.761 0.761 0.766 0.765
## [7071] 0.762 0.761 0.761 0.766 0.762 0.762 0.762 0.761 0.765 0.765
## [7081] 0.762 0.762 0.761 0.765 0.765 0.762 0.761 0.761 0.765 0.765
## [7091] 0.762 0.762 0.761 0.765 0.762 0.762 0.761 0.765 0.762 0.761
## [7101] 0.761 0.765 0.762 0.762 0.761 0.765 0.762 0.761 0.761 0.945
## [7111] 0.762 0.762 0.761 0.892 1.161 0.762 0.762 0.880 1.158 1.270
## [7121] 1.169 0.833 0.821 1.011 1.321 0.827 0.854 1.288 0.995 0.956
## [7131] 1.261 0.946 1.296 0.899 1.205 0.955 1.175 0.832 1.298 0.839
## [7141] 1.316 0.873 1.197 0.853 0.767 1.146 0.825 0.762 0.765 1.226
## [7151] 0.765 0.762 0.815 0.768 0.767 0.765 0.762 0.765 0.762 0.762
## [7161] 0.761 0.765 0.761 0.765 0.762 0.765 0.761 0.765 0.761 0.765
## [7171] 0.762 0.765 0.762 0.762 0.761 0.761 0.765 0.762 0.762 0.761
## [7181] 0.761 0.765 0.762 0.762 0.761 0.762 0.765 0.762 0.761 0.761
## [7191] 0.762 0.765 0.762 0.761 0.761 0.762 0.765 0.762 0.762 0.761
## [7201] 0.763 0.765 0.762 0.762 0.761 0.762 0.761 0.762 0.762 0.761
## [7211] 0.763 0.765 0.762 0.762 0.761 0.763 0.765 0.762 0.762 0.763
## [7221] 0.764 0.765 0.762 0.762 0.765 0.762 0.764 0.762 0.761 0.762
## [7231] 0.765 0.765 0.762 0.762 0.763 0.764 0.765 0.762 0.762 0.762
## [7241] 0.804 0.761 0.762 0.768 0.761 0.762 0.765 0.762 0.762 0.762
## [7251] 0.761 0.764 0.765 0.762 0.762 0.762 0.762 0.765 0.765 0.762
## [7261] 0.762 0.762 0.761 0.765 0.765 0.762 0.762 0.762 0.761 0.765
## [7271] 0.765 0.762 0.762 0.762 0.761 0.765 0.765 0.762 0.762 0.762
## [7281] 0.761 0.765 0.761 0.762 0.762 0.762 0.761 0.765 0.765 0.762
## [7291] 0.762 0.762 0.761 0.765 0.765 0.762 0.762 0.762 0.766 0.762
## [7301] 0.762 0.762 0.762 0.762 0.761 0.765 0.762 0.762 0.762 0.762
## [7311] 0.762 0.765 0.762 0.762 0.762 0.762 0.761 0.765 0.765 0.762
## [7321] 0.762 0.805 0.761 0.765 0.765 0.762 0.762 0.761 0.761 0.765
## [7331] 0.765 0.762 0.762 0.784 0.761 0.765 0.761 0.762 0.762 0.762
## [7341] 0.761 0.765 0.765 0.762 0.762 0.762 0.761 0.765 0.765 0.762
## [7351] 0.762 0.761 0.783 0.765 0.765 0.762 0.762 0.762 0.761 0.765
## [7361] 0.762 0.762 0.762 0.761 0.765 0.762 0.762 0.761 0.761 0.765
## [7371] 0.762 0.762 0.780 0.761 0.765 0.762 0.761 0.761 0.765 0.762
## [7381] 0.762 0.761 0.765 0.762 0.762 0.761 0.765 0.762 0.762 0.761
## [7391] 0.765 0.762 0.761 0.781 0.765 0.762 0.762 0.765 0.761 0.765
## [7401] 0.762 0.762 0.765 0.762 0.765 0.761 0.765 0.762 0.765 0.765
## [7411] 0.762 0.765 0.765 0.762 0.761 0.765 0.761 0.765 0.762 0.762
## [7421] 0.765 0.762 0.762 0.762 0.761 0.765 0.761 0.765 0.762 0.762
## [7431] 0.765 0.761 0.765 0.762 0.761 0.765 0.761 0.765 0.762 0.762
## [7441] 0.762 0.761 0.761 0.765 0.762 0.762 0.762 0.761 0.762 0.765
## [7451] 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762 0.761 0.761
## [7461] 0.761 0.765 0.762 0.762 0.761 0.761 0.761 0.765 0.762 0.762
## [7471] 0.762 0.761 0.761 0.765 0.762 0.762 0.762 0.761 0.762 0.761
## [7481] 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762 0.762 0.761
## [7491] 0.761 0.765 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762
## [7501] 0.762 0.769 0.762 0.763 0.762 0.762 0.762 0.761 0.762 0.765
## [7511] 0.762 0.762 0.762 0.761 0.762 0.765 0.762 0.762 0.762 0.761
## [7521] 0.773 0.761 0.762 0.762 0.764 0.762 0.943 0.763 0.763 0.795
## [7531] 0.762 0.970 0.763 0.788 0.950 0.907 0.913 0.763 0.763 0.827
## [7541] 0.764 0.773 0.763 0.763 0.763 0.826 0.765 0.763 0.763 1.340
## [7551] 0.855 0.797 0.763 0.952 0.872 0.815 0.862 0.763 0.763 0.832
## [7561] 0.874 0.841 0.763 0.763 0.787 0.906 0.810 0.763 0.763 0.848
## [7571] 0.762 0.849 0.763 0.763 0.981 0.824 0.762 0.763 0.763 0.763
## [7581] 0.843 0.764 0.763 0.865 0.763 0.803 0.764 0.763 0.763 0.845
## [7591] 0.772 0.764 0.763 0.763 0.761 0.764 0.785 0.763 0.763 0.991
## [7601] 0.845 0.801 0.763 0.763 0.792 0.901 0.764 0.763 0.763 0.802
## [7611] 0.762 0.819 0.764 0.763 0.763 0.761 0.762 0.842 0.770 0.763
## [7621] 0.966 0.811 0.826 0.761 0.763 0.763 0.763 0.765 0.763 0.763
## [7631] 0.762 0.912 0.763 0.763 0.762 0.912 0.763 0.762 1.035 0.763
## [7641] 0.885 0.762 1.036 0.761 0.763 0.762 1.081 0.762 1.038 1.457
## [7651] 0.857 0.762 0.973 0.762 1.036 0.946 0.766 0.762 1.072 1.054
## [7661] 1.059 0.909 0.763 0.762 1.059 0.761 1.018 0.890 0.762 1.058
## [7671] 0.761 1.044 0.815 0.924 1.006 0.761 1.034 0.828 0.870 1.059
## [7681] 0.761 0.970 0.886 0.987 1.019 0.761 1.002 0.917 0.851 0.910
## [7691] 0.761 0.881 0.869 1.047 0.764 0.762 0.920 0.808 0.806 0.764
## [7701] 0.762 0.762 0.788 0.763 0.764 0.853 0.762 0.765 0.764 0.762
## [7711] 0.825 0.760 0.763 0.764 0.762 0.832 0.763 0.766 0.995 0.762
## [7721] 0.764 0.763 0.763 1.048 0.762 0.768 0.763 0.763 0.932 0.762
## [7731] 0.827 0.763 0.763 0.929 0.762 0.791 0.763 0.763 0.930 0.762
## [7741] 0.799 0.763 0.763 0.763 0.762 0.790 0.763 0.763 0.765 0.762
## [7751] 0.761 0.763 0.763 0.763 0.762 0.764 0.763 0.763 0.788 0.761
## [7761] 0.764 0.763 0.763 0.896 0.762 0.764 0.763 0.763 0.858 0.762
## [7771] 0.848 0.763 0.873 0.902 0.762 1.036 0.763 0.783 0.843 0.762
## [7781] 0.909 0.763 0.763 0.872 0.762 0.906 0.763 0.763 0.762 0.763
## [7791] 0.762 1.034 1.049 1.046 0.858 0.762 1.044 1.049 1.023 0.876
## [7801] 0.762 1.072 1.053 1.042 1.038 1.032 0.896 1.044 0.983 0.976
## [7811] 1.084 0.762 0.951 1.052 0.987 1.015 1.046 0.993 1.061 0.987
## [7821] 1.031 1.077 0.762 1.013 1.037 1.052 1.018 1.056 0.855 1.059
## [7831] 1.036 1.024 1.073 0.762 0.764 1.039 1.027 1.002 1.044 0.997
## [7841] 0.804 1.023 0.989 0.993 1.087 0.762 0.986 1.046 1.002 1.057
## [7851] 1.087 0.766 1.040 1.051 1.004 0.925 0.764 0.898 1.042 1.046
## [7861] 0.874 0.775 1.022 1.009 1.046 0.879 0.884 1.005 1.002 1.042
## [7871] 0.876 0.764 0.969 0.954 0.956 0.864 0.816 0.998 0.893 0.953
## [7881] 0.833 0.818 1.061 0.984 1.029 0.908 0.767 1.042 1.054 1.050
## [7891] 0.911 0.765 0.761 1.021 1.020 0.907 0.762 0.782 0.761 1.042
## [7901] 1.050 0.853 0.768 0.761 1.039 1.090 0.839 0.764 0.761 1.011
## [7911] 0.762 0.989 0.762 0.799 0.761 1.006 0.762 0.910 0.762 0.943
## [7921] 5.591 1.031 0.762 1.005 0.762 0.944 1.637 1.004 0.762 0.942
## [7931] 0.762 0.971 0.976 1.015 0.762 1.080 0.761 0.997 0.976 0.975
## [7941] 0.762 1.018 0.761 0.985 1.007 0.762 1.015 0.761 1.014 1.014
## [7951] 0.976 0.913 0.761 0.999 1.003 1.036 1.028 0.761 1.049 1.030
## [7961] 0.998 0.945 0.761 1.025 1.023 1.025 1.057 0.761 1.040 1.050
## [7971] 0.993 0.935 0.762 0.806 1.031 1.013 0.908 0.920 1.040 1.008
## [7981] 0.982 0.950 0.762 0.975 0.923 0.762 0.970 1.050 1.014 0.770
## [7991] 0.960 0.762 0.906 1.053 1.006 1.059 0.762 1.009 1.052 1.006
## [8001] 1.085 0.762 1.001 1.039 0.984 1.080 0.762 0.762 0.917 1.046
## [8011] 1.032 1.073 0.762 0.762 1.051 1.044 1.001 1.061 0.762 0.762
## [8021] 1.043 1.050 1.005 1.047 0.762 1.061 1.032 0.910 1.079 0.762
## [8031] 0.762 0.822 1.009 1.009 1.049 0.762 0.762 0.938 1.055 0.995
## [8041] 1.048 0.761 0.987 1.030 1.046 0.969 0.762 0.858 1.028 1.045
## [8051] 1.044 0.762 0.762 0.851 1.040 1.057 1.082 0.762 0.762 1.023
## [8061] 1.029 1.021 1.051 0.762 1.039 1.033 1.021 1.016 0.762 1.018
## [8071] 1.032 1.007 0.762 0.818 0.818 0.792 0.762 0.832 0.846 0.762
## [8081] 0.811 0.824 0.762 0.761 0.817 0.762 0.817 0.816 0.762 0.828
## [8091] 0.818 0.762 0.846 0.802 0.762 0.834 0.817 0.762 0.828 0.827
## [8101] 0.828 0.819 0.762 0.807 0.823 0.762 0.813 0.817 0.762 0.838
## [8111] 0.831 0.761 0.849 0.813 0.761 0.847 0.821 0.762 0.835 0.830
## [8121] 0.762 0.822 0.818 0.761 0.830 0.831 0.762 0.828 0.816 0.762
## [8131] 0.822 0.823 0.761 0.830 0.815 0.762 0.800 0.810 0.761 0.830
## [8141] 0.818 0.762 0.817 0.806 0.762 0.809 0.812 0.762 0.807 0.805
## [8151] 0.761 0.826 0.828 0.762 0.761 0.802 0.761 0.761 0.821 0.762
## [8161] 0.761 0.818 0.864 0.761 0.845 0.761 0.828 0.761 0.781 0.815
## [8171] 0.856 0.803 0.785 0.801 0.824 0.800 0.807 0.811 0.804 0.798
## [8181] 0.812 0.805 0.796 0.813 0.806 0.830 0.812 0.821 0.803 0.805
## [8191] 0.767 0.835 0.834 0.810 0.847 0.799 0.803 0.797 0.804 0.802
## [8201] 0.782 0.805 0.815 0.817 0.848 0.817 0.809 0.761 0.762 0.761
## [8211] 0.762 0.762 0.762 0.762 0.762 0.761 0.762 0.761 0.762 0.761
## [8221] 0.762 0.761 0.762 0.761 0.762 0.766 0.762 0.761 0.762 0.763
## [8231] 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [8241] 0.762 0.761 0.761 0.761 0.762 0.761 0.762 0.761 0.761 0.761
## [8251] 0.762 0.763 0.761 0.761 0.762 0.761 0.762 0.761 0.762 0.761
## [8261] 0.761 0.798 0.762 0.761 0.761 0.927 0.762 0.765 0.766 0.765
## [8271] 0.761 0.767 0.762 0.765 0.761 0.766 0.762 0.765 0.761 0.765
## [8281] 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761
## [8291] 0.762 0.761 0.762 0.761 0.762 0.761 0.762 0.761 0.762 0.762
## [8301] 0.762 0.761 0.761 0.765 0.762 0.761 0.762 0.761 0.761 0.784
## [8311] 0.783 0.762 0.762 0.791 0.815 0.828 0.762 0.762 0.776 0.795
## [8321] 0.814 0.762 0.762 0.830 0.822 0.773 0.762 0.762 0.761 0.845
## [8331] 0.798 0.762 0.762 0.776 0.846 0.773 0.762 0.762 0.764 0.834
## [8341] 0.761 0.762 0.762 0.804 0.815 0.852 0.762 0.762 0.772 0.824
## [8351] 0.858 0.762 0.762 0.812 0.822 0.839 0.762 0.762 0.815 0.835
## [8361] 0.847 0.762 0.762 0.837 0.858 0.797 0.762 0.762 0.825 0.842
## [8371] 0.859 0.762 0.762 0.845 0.843 0.860 0.762 0.761 0.859 0.780
## [8381] 0.843 0.762 0.761 0.852 0.823 0.838 0.762 0.854 0.847 0.856
## [8391] 0.762 0.815 0.849 0.840 0.761 0.822 0.827 0.830 0.762 0.826
## [8401] 0.812 0.761 0.762 0.827 0.826 0.761 0.761 0.818 0.846 0.761
## [8411] 0.762 0.812 0.851 0.761 0.761 0.841 1.072 0.761 0.762 0.825
## [8421] 0.831 0.766 0.762 0.838 1.117 1.119 0.762 1.316 1.195 1.073
## [8431] 1.233 1.367 1.512 1.272 1.201 1.280 1.487 1.206 1.136 1.311
## [8441] 1.310 1.081 1.224 1.307 1.186 1.325 1.330 1.192 1.296 1.321
## [8451] 1.230 0.859 1.289 0.918 0.761 0.851 0.761 0.835 0.855 0.847
## [8461] 0.762 0.840 0.825 0.850 0.761 0.850 0.826 0.842 0.762 0.841
## [8471] 0.830 0.851 0.761 0.856 0.826 0.861 0.762 0.762 0.853 0.836
## [8481] 0.827 0.762 0.762 0.857 0.812 0.840 0.762 0.762 0.854 0.821
## [8491] 0.863 0.762 0.761 0.845 0.835 0.863 0.762 0.761 0.851 0.832
## [8501] 0.856 0.762 0.762 0.843 0.839 0.861 0.762 0.762 0.847 0.841
## [8511] 0.763 0.762 0.762 0.856 0.850 0.772 0.762 0.762 0.830 0.837
## [8521] 0.816 0.762 0.762 0.822 0.808 0.808 0.762 0.762 0.839 0.808
## [8531] 0.849 0.762 0.761 0.823 0.835 0.851 0.762 0.762 0.800 0.785
## [8541] 0.819 0.762 0.762 0.785 0.820 0.765 0.762 0.783 0.761 0.762
## [8551] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [8561] 0.762 0.762 0.761 0.761 0.762 0.762 1.706 0.761 0.762 0.761
## [8571] 0.762 0.761 0.762 0.762 0.762 0.761 0.761 0.762 0.762 0.762
## [8581] 0.762 0.762 0.762 0.761 0.762 0.762 0.761 0.761 0.761 0.761
## [8591] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8601] 0.761 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761
## [8611] 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [8621] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [8631] 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761
## [8641] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761
## [8651] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8661] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761
## [8671] 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.762 0.761
## [8681] 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8691] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8701] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8711] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [8721] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8731] 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8741] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8751] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8761] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761
## [8771] 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 1.579 1.875
## [8781] 0.761 1.100 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [8791] 0.761 0.805 0.795 0.787 0.774 0.765 0.769 0.772 0.768 0.777
## [8801] 0.775 0.767 0.775 0.770 0.772 0.776 0.784 0.776 0.784 0.776
## [8811] 0.784 0.768 0.770 0.775 0.775 0.770 0.768 0.773 0.771 0.771
## [8821] 0.772 0.771 0.781 0.773 0.768 0.766 0.778 0.771 0.803 0.771
## [8831] 0.781 0.778 0.771 0.770 0.771 0.780 0.775 0.780 0.772 0.780
## [8841] 0.771 0.768 0.770 0.775 0.781 0.785 0.775 0.771 0.767 0.773
## [8851] 0.771 0.770 0.774 0.773 0.772 0.769 0.770 0.766 0.768 0.775
## [8861] 0.770 0.782 0.768 0.768 0.772 0.769 0.779 0.772 0.775 0.781
## [8871] 0.778 0.787 0.784 0.779 0.771 0.779 0.791 0.786 0.773 0.777
## [8881] 0.781 0.782 0.784 0.783 0.773 0.775 0.783 0.782 0.773 0.778
## [8891] 0.774 0.768 0.771 0.768 0.780 0.776 0.782 0.794 0.785 0.781
## [8901] 0.783 0.782 0.792 0.788 0.786 0.787 0.776 0.774 0.764 0.766
## [8911] 0.766 0.763 0.766 0.768 0.768 0.767 0.766 0.769 0.770 0.770
## [8921] 0.771 0.766 0.764 0.779 0.831 0.819 0.768 0.779 0.775 0.769
## [8931] 0.770 0.767 0.767 0.770 0.767 0.767 0.769 0.769 0.769 0.765
## [8941] 0.766 0.774 0.772 0.776 0.782 0.773 0.769 0.772 0.770 0.770
## [8951] 0.771 0.771 0.769 0.770 0.767 0.768 0.817 0.778 0.777 0.768
## [8961] 0.779 0.778 0.775 0.770 0.780 0.779 0.785 0.779 0.770 0.777
## [8971] 0.772 0.776 0.783 0.781 0.775 0.774 0.776 0.771 0.785 0.797
## [8981] 0.795 0.775 0.772 0.774 0.775 0.783 0.774 0.776 0.785 0.785
## [8991] 0.786 0.785 0.778 0.777 0.773 0.779 0.774 0.770 0.778 0.780
## [9001] 0.770 0.776 0.774 0.772 0.773 0.771 0.770 0.768 0.773 0.770
## [9011] 0.770 0.773 0.774 0.765 0.769 0.766 0.766 0.777 0.777 0.773
## [9021] 0.768 0.772 0.769 0.778 0.776 0.778 0.773 0.777 0.773 0.771
## [9031] 0.772 0.765 0.765 0.765 0.764 0.764 0.763 0.764 0.765 0.763
## [9041] 0.765 0.763 0.782 1.418 0.763 0.763 0.764 0.765 0.763 0.763
## [9051] 0.764 0.763 0.766 0.763 0.764 0.766 0.765 0.769 0.764 0.765
## [9061] 0.765 0.764 0.765 0.767 0.765 0.767 0.764 0.765 0.762 0.765
## [9071] 0.764 0.764 0.764 0.764 0.765 0.767 0.766 0.768 0.767 0.764
## [9081] 0.766 0.764 0.765 0.764 0.767 0.767 0.765 0.763 0.764 0.769
## [9091] 0.770 0.766 0.763 0.767 0.765 0.764 0.763 0.764 0.768 0.765
## [9101] 0.766 0.766 0.767 0.768 0.763 0.764 0.765 0.766 0.763 0.763
## [9111] 0.763 0.763 0.763 0.764 0.765 0.764 0.763 0.765 0.768 0.765
## [9121] 0.763 0.764 0.764 0.765 0.763 0.764 0.783 0.771 0.792 0.763
## [9131] 0.773 0.778 0.776 0.767 0.767 0.766 0.766 0.764 0.770 0.768
## [9141] 0.779 0.808 0.821 0.799 0.776 0.801 0.818 0.808 0.815 0.811
## [9151] 0.773 0.770 0.766 0.767 0.773 0.768 0.776 0.774 0.772 0.773
## [9161] 0.773 0.765 0.762 0.763 0.768 0.767 0.763 0.762 0.764 0.763
## [9171] 0.763 0.767 0.765 0.764 0.763 0.766 0.763 0.765 0.763 0.764
## [9181] 0.764 0.763 0.768 0.765 0.765 0.765 0.766 0.763 0.763 0.764
## [9191] 0.765 0.763 0.765 0.765 0.763 0.772 0.764 0.764 0.764 0.763
## [9201] 0.763 0.763 0.765 0.764 0.764 0.765 0.764 0.763 0.763 0.762
## [9211] 0.762 0.762 0.762 0.762 0.763 0.762 0.763 0.764 0.763 0.763
## [9221] 0.763 0.762 0.763 0.763 0.764 0.764 0.763 0.763 0.770 0.776
## [9231] 0.764 0.762 0.784 0.801 0.782 0.786 0.803 0.780 0.818 0.836
## [9241] 0.808 0.787 0.798 0.833 0.803 0.779 0.786 0.783 0.763 0.781
## [9251] 0.763 0.779 0.772 0.772 0.784 0.781 0.771 0.774 0.794 0.790
## [9261] 0.774 0.770 0.790 0.767 0.765 0.764 0.814 0.766 0.790 0.762
## [9271] 0.787 0.764 0.779 0.795 0.771 0.788 0.792 0.807 0.782 0.818
## [9281] 0.834 0.815 0.817 0.811 0.776 0.764 0.781 0.763 0.773 0.767
## [9291] 0.772 0.763 0.762 0.785 0.768 0.776 0.771 0.763 0.762 0.762
## [9301] 0.764 0.763 0.762 0.764 0.762 0.766 0.762 0.871 0.763 0.855
## [9311] 0.779 0.848 0.821 0.831 0.811 0.777 0.784 0.902 0.764 0.766
## [9321] 0.761 0.762 0.764 0.778 0.762 0.765 0.762 0.766 0.804 0.768
## [9331] 0.768 0.784 0.767 0.765 0.777 0.774 0.780 0.800 0.762 0.761
## [9341] 0.766 0.761 0.782 0.791 0.810 0.792 0.797 0.776 0.771 0.786
## [9351] 0.788 0.763 0.800 0.801 0.770 0.763 0.765 0.768 0.776 0.773
## [9361] 0.769 0.797 0.772 0.776 0.761 0.774 0.794 0.770 0.773 0.798
## [9371] 0.761 0.765 0.807 0.805 0.768 0.772 0.809 0.805 0.762 0.770
## [9381] 0.773 0.762 0.775 0.764 0.784 0.761 0.792 0.782 0.772 0.793
## [9391] 0.770 0.780 0.764 0.788 0.803 0.774 0.770 0.775 0.766 0.763
## [9401] 0.775 0.779 0.763 0.776 0.782 0.764 0.778 0.782 0.762 0.774
## [9411] 0.790 0.762 0.762 0.762 0.762 0.771 0.762 0.767 0.762 0.766
## [9421] 0.762 0.761 0.762 0.761 0.761 0.894 0.921 1.057 1.107 1.097
## [9431] 1.068 1.101 1.132 1.062 0.843 0.762 0.762 0.762 0.762 0.762
## [9441] 0.764 0.761 0.763 0.762 0.762 0.762 0.762 0.772 0.769 0.784
## [9451] 0.770 0.770 0.769 0.774 0.761 0.761 0.761 0.763 0.763 0.763
## [9461] 0.763 0.763 0.761 0.761 0.953 0.923 0.924 0.963 0.801 0.762
## [9471] 0.893 0.829 0.762 0.809 0.770 0.772 0.794 0.801 0.779 0.781
## [9481] 0.762 0.769 0.762 0.762 0.762 0.762 0.761 0.762 0.762 0.761
## [9491] 0.761 0.761 0.761 0.761 0.776 0.773 0.771 0.776 0.771 0.783
## [9501] 0.772 0.771 0.772 0.771 0.772 0.766 0.772 0.770 0.769 0.766
## [9511] 0.775 0.768 0.774 0.777 0.772 1.023 0.979 0.932 0.849 0.975
## [9521] 0.940 0.821 0.772 0.783 0.773 0.785 0.794 0.778 0.778 0.777
## [9531] 0.778 0.780 0.792 0.814 0.798 0.883 0.849 0.835 0.848 0.902
## [9541] 0.872 0.897 0.839 0.845 0.839 0.845 0.857 0.852 0.837 0.848
## [9551] 0.825 0.814 0.843 0.822 0.796 0.832 0.798 0.832 0.816 0.811
## [9561] 0.804 0.784 0.816 0.789 0.785 0.782 0.799 0.787 0.799 0.796
## [9571] 0.789 0.780 0.775 0.796 0.837 0.761 0.789 0.876 0.766 0.797
## [9581] 0.765 0.836 0.818 0.822 0.762 0.762 0.845 0.762 0.895 0.869
## [9591] 0.801 0.762 0.908 0.762 0.761 0.834 0.845 0.816 0.828 0.838
## [9601] 0.815 0.813 0.800 0.817 0.824 0.767 0.822 0.841 0.833 0.839
## [9611] 0.829 0.828 0.837 0.845 0.821 0.777 0.766 0.764 1.217 1.524
## [9621] 1.218 1.250 1.294 1.265 1.175 1.124 1.246 1.080 1.162 1.117
## [9631] 1.185 1.128 1.124 1.205 1.114 1.117 1.065 2.371 2.893 2.777
## [9641] 3.497 3.529 2.143 1.739 1.964 1.736 2.037 2.069 1.584 1.128
## [9651] 1.096 1.189 1.185 1.904 0.761 0.761 0.761 0.761 0.761 0.761
## [9661] 1.417 1.860 0.761 0.761 6.832 8.015 7.050 6.124 4.640 2.892
## [9671] 0.878 0.876 0.874 0.863 0.872 0.832 0.864 0.853 0.863 0.872
## [9681] 0.865 0.877 0.860 0.886 1.523 1.629 1.661 1.664 1.489 0.831
## [9691] 0.775 0.781 0.819 0.786 0.882 0.904 0.855 0.769 0.886 0.886
## [9701] 0.855 0.776 0.808 0.793 0.765 0.766 0.804 0.805 0.761 0.761
## [9711] 0.761 0.761 0.762 0.761 0.761 0.762 0.764 0.762 0.761 0.789
## [9721] 0.762 0.771 0.762 0.764 0.789 0.857 0.766 0.774 0.761 0.765
## [9731] 0.808 0.805 0.761 0.924 0.774 0.836 0.870 0.891 0.814 1.174
## [9741] 0.999 1.064 0.935 1.033 0.774 0.810 0.795 0.761 0.794 0.824
## [9751] 0.854 0.767 0.770 0.767 1.012 0.970 1.046 1.048 0.849 1.111
## [9761] 1.087 1.114 1.127 0.830 0.870 0.891 1.150 0.810 0.839 0.811
## [9771] 0.871 0.824 0.863 1.388 0.842 0.887 0.988 0.778 0.855 0.800
## [9781] 0.798 0.826 0.797 0.812 0.875 0.824 0.807 0.811 0.899 0.857
## [9791] 0.782 0.971 0.780 0.927 0.862 0.782 0.859 0.779 0.917 0.782
## [9801] 0.950 0.778 0.940 0.778 0.977 0.778 0.785 1.448 1.344 1.125
## [9811] 1.122 1.142 1.112 1.162 1.113 1.096 1.140 2.148 0.843 0.858
## [9821] 0.824 0.792 0.819 0.806 0.839 0.865 0.789 0.794 0.792 0.789
## [9831] 0.784 0.787 0.787 0.799 0.785 0.781 0.796 0.791 0.783 0.809
## [9841] 0.761 1.526 1.186 1.128 1.350 1.177 1.208 1.252 1.279 1.095
## [9851] 1.114 1.049 1.083 1.176 1.092 1.030 0.961 0.932 1.213 1.155
## [9861] 1.106 1.146 1.200 1.082 1.176 0.979 0.982 0.989 1.126 1.007
## [9871] 0.939 1.108 1.028 0.973 0.818 0.785 1.051 1.215 1.410 0.861
## [9881] 1.265 1.250 1.317 1.052 1.243 1.173 1.053 1.058 1.222 1.268
## [9891] 1.275 1.532 1.964 1.028 1.464 0.824 0.830 1.199 0.792 0.848
## [9901] 0.798 1.271 1.131 0.785 1.458 1.270 1.243 1.237 1.078 0.999
## [9911] 0.861 1.003 1.017 1.054 0.977 1.019 1.014 1.024 1.113 1.109
## [9921] 1.159 0.896 0.801 0.852 1.889 0.950 1.222 1.081 1.017 1.086
## [9931] 0.806 0.915 1.240 1.116 1.240 1.173 1.092 1.118 1.030 0.989
## [9941] 0.761 1.361 1.242 1.258 0.837 0.839 0.772 1.159 1.049 1.055
## [9951] 1.261 0.988 0.864 0.869 0.992 0.828 0.825 0.768 0.796 0.818
## [9961] 0.858 0.787 0.895 0.778 0.860 0.828 0.822 0.761 0.761 0.761
## [9971] 0.761 0.761 0.761 1.239 1.376 1.150 1.438 1.136 1.178 1.317
## [9981] 1.869 1.414 1.448 1.554 0.999 1.199 1.166 1.204 1.120 1.237
## [9991] 1.249 1.316 1.255 1.303 1.296 1.253 1.329 1.281 1.112 1.077
## [10001] 1.409 1.277 1.321 0.866 0.816 0.837 0.927 0.897 0.913 0.889
## [10011] 0.871 0.866 0.872 0.825 0.854 0.926 0.973 0.991 0.865 0.826
## [10021] 0.857 0.893 0.941 0.904 0.873 0.947 0.969 0.963 0.955 0.924
## [10031] 0.904 0.927 0.988 0.902 0.914 1.002 0.973 0.762 1.175 1.198
## [10041] 1.102 1.062 0.914 0.986 0.935 1.162 0.761 0.811 0.801 0.789
## [10051] 1.225 1.219 2.179 1.704 1.797 1.455 1.561 0.951 1.825 1.100
## [10061] 0.948 1.287 0.952 0.964 1.250 1.047 1.173 1.062 1.132 1.180
## [10071] 1.012 1.102 1.030 1.103 1.018 1.020 1.011 1.018 0.761 1.002
## [10081] 0.762 1.032 0.761 0.962 0.762 1.018 0.761 1.034 0.761 1.219
## [10091] 0.761 1.012 0.761 1.023 0.764 1.028 0.761 1.389 0.761 1.837
## [10101] 0.764 1.324 0.762 1.138 0.762 2.038 0.761 1.770 0.761 1.444
## [10111] 0.761 1.779 0.761 1.405 0.761 1.451 0.761 1.421 1.919 0.813
## [10121] 1.040 0.818 0.825 0.904 0.885 1.365 0.967 0.935 0.967 1.258
## [10131] 1.229 1.021 1.220 1.330 1.225 0.971 1.302 1.120 1.078 1.079
## [10141] 0.950 0.779 0.975 1.367 1.502 1.486 1.660 1.635 1.273 1.469
## [10151] 1.376 1.212 0.836 0.893 1.264 1.243 0.913 1.328 1.287 1.259
## [10161] 0.910 0.877 1.283 1.207 1.113 1.307 1.296 1.203 0.990 1.805
## [10171] 1.342 1.404 1.312 1.313 1.307 1.251 0.834 0.827 0.806 0.797
## [10181] 1.230 1.362 1.354 1.347 1.172 0.889 0.826 0.829 0.860 0.761
## [10191] 0.761 0.761 0.761 0.761 1.052 1.138 1.174 0.761 0.954 0.761
## [10201] 1.091 0.761 1.304 0.762 1.332 0.761 1.221 0.761 1.227 1.226
## [10211] 1.069 1.105 1.244 1.260 1.213 1.203 0.761 0.787 0.761 0.917
## [10221] 0.979 0.821 0.762 0.763 0.761 0.763 0.761 0.761 0.762 0.762
## [10231] 0.761 0.762 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.761
## [10241] 0.761 0.761 0.761 0.761 0.771 0.896 1.170 0.983 0.943 0.923
## [10251] 0.775 0.872 1.093 0.937 0.765 1.131 1.015 1.139 0.789 0.949
## [10261] 0.966 0.890 0.977 0.788 0.845 0.838 0.775 0.842 0.799 0.866
## [10271] 1.224 0.861 1.121 0.761 0.813 0.816 0.800 0.772 0.779 0.818
## [10281] 0.833 0.792 0.815 0.781 0.797 0.793 0.795 0.812 0.794 0.881
## [10291] 0.802 0.941 0.802 0.861 0.813 0.883 0.805 0.874 0.795 0.838
## [10301] 0.846 0.850 0.785 0.844 0.798 0.831 0.824 0.847 0.810 0.965
## [10311] 0.847 0.778 0.778 0.775 0.821 0.786 0.767 0.780 0.777 0.767
## [10321] 0.763 0.766 0.772 0.761 0.778 0.812 0.776 0.769 0.773 0.779
## [10331] 0.783 0.809 0.796 0.824 0.799 0.800 0.792 0.813 0.810 1.019
## [10341] 1.093 1.270 1.301 1.196 1.106 1.164 1.044 1.105 1.090 1.301
## [10351] 2.025 1.243 1.420 1.322 1.484 1.428 1.365 1.372 1.153 2.226
## [10361] 1.458 1.259 1.416 1.473 1.637 1.473 1.103 1.278 1.342 0.897
## [10371] 1.229 1.146 0.857 1.270 1.418 1.738 1.216 1.363 1.037 1.272
## [10381] 1.286 1.402 1.827 1.428 1.750 1.723 1.581 1.604 1.604 1.894
## [10391] 1.995 1.423 1.451 1.255 1.147 1.276 1.275 1.369 1.504 1.368
## [10401] 1.308 1.401 1.615 1.083 1.528 0.988 1.191 1.636 1.570 1.120
## [10411] 0.803 0.787 0.769 0.765 0.766 0.776 0.792 0.761 0.767 0.848
## [10421] 0.761 1.016 0.764 1.134 0.864 1.127 0.890 1.061 0.826 0.986
## [10431] 1.685 1.785 0.776 1.272 1.350 1.338 0.876 1.118 0.868 0.767
## [10441] 0.783 0.786 0.780 0.795 0.855 0.849 0.802 0.868 0.885 0.817
## [10451] 0.815 0.775 0.762 0.761 1.882 1.703 1.134 1.005 1.445 1.317
## [10461] 1.073 0.995 1.266 1.359 1.179 1.147 1.151 1.149 1.229 1.498
## [10471] 1.287 1.052 1.025 0.897 1.121 1.203 1.252 1.171 1.236 1.142
## [10481] 1.338 1.094 0.889 0.996 1.031 0.851 0.972 1.057 1.310 1.269
## [10491] 0.992 1.095 1.192 1.143 1.203 1.113 1.190 0.899 1.145 1.183
## [10501] 1.249 1.258 1.115 1.043 1.013 1.102 1.172 1.156 0.992 0.989
## [10511] 0.765 0.841 0.928 0.767 0.806 1.429 0.765 0.765 0.762 1.493
## [10521] 0.761 0.761 0.765 0.762 0.762 0.762 0.765 0.761 0.761 0.944
## [10531] 0.761 0.765 0.765 1.641 2.219 1.550 0.975 0.797 0.785 0.989
## [10541] 0.878 0.906 1.651 1.645 1.966 1.408 0.815 0.769 0.769 0.769
## [10551] 0.765 0.774 0.766 0.870 1.534 0.885 0.765 0.780 0.789 0.812
## [10561] 0.856 0.912 0.841 0.799 0.893 0.925 1.018 0.960 0.972 1.124
## [10571] 1.021 0.977 1.094 0.839 0.917 0.853 0.788 0.861 1.023 1.180
## [10581] 1.063 1.260 1.137 1.321 1.315 1.150 0.924 0.864 0.839 0.876
## [10591] 0.913 1.017 0.922 1.050 1.425 1.082 0.928 1.006 0.866 1.098
## [10601] 1.070 1.051 1.116 1.028 1.080 1.202 0.863 1.057 1.046 1.774
## [10611] 1.581 2.157 2.379 2.570 1.447 1.583 1.186 1.055 1.326 1.479
## [10621] 1.294 1.280 0.940 0.998 1.038 1.049 1.053 0.972 0.763 0.763
## [10631] 0.763 0.763 0.763 0.761 0.762 0.761 0.763 0.763 0.763 0.763
## [10641] 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.901 0.761 0.830
## [10651] 0.761 0.950 0.761 0.980 0.763 1.051 0.763 1.608 0.761 1.870
## [10661] 0.761 2.792 0.761 1.755 0.761 1.198 0.761 1.422 0.762 1.572
## [10671] 0.761 1.519 0.761 1.188 0.761 1.050 0.761 1.063 0.761 0.980
## [10681] 0.762 1.695 0.761 1.311 0.761 1.598 0.761 0.763 0.761 0.761
## [10691] 0.761 0.761 0.761 0.763 0.761 0.761 0.761 0.761 0.761 0.761
## [10701] 0.761 0.761 0.761 0.763 0.761 0.761 0.761 0.763 0.763 0.763
## [10711] 0.998 1.213 1.565 2.601 2.785 2.281 1.741 2.324 2.323 1.992
## [10721] 1.904 2.449 1.937 3.336 0.761 0.931 1.191 1.260 0.965 1.101
## [10731] 0.874 1.063 1.069 0.868 1.179 1.137 1.197 0.915 1.016 1.087
## [10741] 1.055 1.715 1.477 1.058 1.227 1.099 0.997 1.712 2.001 1.234
## [10751] 1.228 1.255 1.363 1.442 1.400 1.667 1.858 1.729 1.949 2.110
## [10761] 1.735 1.604 1.691 1.707 1.642 1.365 1.700 1.678 0.764 0.761
## [10771] 0.761 1.539 1.157 0.761 1.116 0.798 1.035 0.873 0.913 0.880
## [10781] 0.761 0.761 0.761 0.929 1.014 0.761 1.082 1.039 0.978 1.055
## [10791] 1.075 1.830 1.072 1.085 0.978 1.054 0.971 0.944 0.901 0.986
## [10801] 1.002 1.002 1.051 1.066 1.068 1.379 1.352 1.011 0.761 0.873
## [10811] 0.761 0.761 0.790 1.331 1.467 1.218 2.316 1.800 0.989 1.565
## [10821] 1.328 1.732 1.615 1.373 1.619 1.503 1.270 1.800 1.705 1.491
## [10831] 1.080 0.994 1.002 1.130 0.762 0.761 0.761 0.833 0.804 1.279
## [10841] 1.112 1.035 1.106 1.241 1.232 2.087 2.668 2.327 1.754 1.709
## [10851] 1.430 1.938 1.247 1.903 2.341 1.692 1.778 1.609 1.493 1.445
## [10861] 1.974 1.294 1.178 0.761 0.761 0.761 0.761 0.762 1.089 0.915
## [10871] 1.176 1.159 1.143 3.370 1.154 1.119 1.108 1.034 1.193 1.169
## [10881] 1.191 1.140 1.293 1.196 1.977 0.964 2.504 3.531 1.736 1.425
## [10891] 1.281 1.271 1.960 1.385 1.280 1.433 2.544 1.486 1.857 1.516
## [10901] 1.946 1.397 1.577 1.600 1.731 1.236 1.503 1.758 1.345 1.403
## [10911] 2.263 1.596 2.161 1.299 1.622 1.398 1.833 0.951 1.694 1.439
## [10921] 1.950 0.773 1.871 1.296 1.645 0.878 2.165 2.428 2.134 1.255
## [10931] 1.378 1.362 1.370 1.319 1.404 1.283 1.275 1.921 1.764 1.422
## [10941] 0.768 0.761 0.818 0.761 0.964 0.894 0.773 0.854 0.957 0.761
## [10951] 0.761 0.899 2.089 0.965 1.390 0.992 1.018 1.009 1.008 1.040
## [10961] 1.169 0.989 1.307 0.991 1.398 0.914 1.034 1.030 1.000 1.285
## [10971] 1.259 1.310 1.277 1.186 1.170 1.300 1.190 1.164 1.517 0.984
## [10981] 0.986 0.925 0.879 0.866 0.906 0.823 0.982 0.779 1.049 0.801
## [10991] 0.783 1.148 1.081 1.145 0.800 1.416 0.761 1.160 1.159 0.992
## [11001] 0.965 0.765 0.779 0.763 0.798 1.071 1.077 2.215 1.558 1.902
## [11011] 2.691 1.651 1.862 2.102 1.783 1.780 1.768 2.278 2.009 1.779
## [11021] 1.898 1.662 1.673 1.808 2.003 1.925 1.760 2.487 2.063 0.933
## [11031] 1.466 1.186 1.134 1.133 0.978 1.446 1.193 1.657 1.402 1.082
## [11041] 1.276 1.058 1.026 1.036 1.159 1.227 0.958 1.054 1.183 1.177
## [11051] 1.241 1.027 0.837 0.949 0.920 0.911 1.037 0.938 0.963 0.924
## [11061] 1.065 1.109 1.087 1.129 1.135 1.076 1.050 0.920 0.934 0.998
## [11071] 1.006 1.063 0.875 0.965 0.980 0.843 1.036 0.907 1.284 0.761
## [11081] 1.451 1.386 1.316 1.193 0.987 1.148 1.029 1.089 1.366 1.531
## [11091] 1.029 1.425 1.048 1.072 0.818 0.761 1.042 1.089 1.105 0.914
## [11101] 1.048 1.106 1.055 0.995 1.044 0.990 1.043 1.066 0.761 0.770
## [11111] 0.826 0.817 0.814 0.793 0.898 1.343 0.852 0.818 1.573 1.574
## [11121] 0.985 1.003 0.761 1.554 0.845 0.939 1.103 1.553 1.658 1.038
## [11131] 1.675 0.845 1.086 0.999 1.294 1.414 0.896 1.503 0.917 1.430
## [11141] 1.546 1.141 1.507 1.466 1.175 0.947 0.761 0.761 1.339 0.881
## [11151] 0.908 1.619 1.882 0.887 0.810 1.218 1.188 1.011 0.953 1.030
## [11161] 1.140 1.074 1.033 0.794 1.169 0.855 0.912 0.905 0.824 0.796
## [11171] 0.856 0.852 0.802 0.786 0.784 0.817 0.833 0.761 0.761 0.958
## [11181] 1.004 1.145 0.765 0.790 0.945 1.144 0.765 0.765 0.762 0.765
## [11191] 1.557 1.243 1.404 1.339 0.762 0.822 0.946 1.224 1.140 1.135
## [11201] 1.235 0.823 1.486 1.229 1.302 1.443 1.791 2.525 1.741 2.043
## [11211] 1.317 1.255 1.251 2.190 0.981 1.362 1.389 1.386 1.232 0.765
## [11221] 0.763 0.766 0.765 0.763 0.763 0.763 0.784 0.891 0.897 1.300
## [11231] 0.962 0.945 1.087 0.924 1.045 1.044 1.003 0.871 0.918 0.895
## [11241] 0.864 0.848 0.974 0.855 0.762 0.762 0.762 0.762 0.761 0.816
## [11251] 0.761 0.768 0.762 0.762 0.762 0.779 0.765 2.671 0.762 2.155
## [11261] 0.762 1.663 0.766 1.628 0.761 1.555 1.612 1.739 1.623 1.703
## [11271] 1.712 1.164 1.150 1.403 1.347 0.762 1.428 0.761 0.762 0.761
## [11281] 0.761 0.761 0.762 0.762 0.761 0.762 0.762 0.762 0.940 1.543
## [11291] 1.549 0.775 0.871 1.628 0.789 0.810 0.765 0.762 0.765 0.761
## [11301] 0.976 0.880 0.867 0.857 0.761 0.785 0.794 0.925 1.284 1.200
## [11311] 1.315 0.785 1.675 1.396 1.580 0.835 0.851 0.813 0.805 0.795
## [11321] 0.761 0.763 1.542 1.569 1.518 0.833 0.792 0.848 0.770 1.602
## [11331] 1.591 0.834 1.415 0.781 0.805 1.521 1.604 0.815 1.587 0.851
## [11341] 1.368 1.510 0.927 1.633 0.790 0.868 0.828 0.809 0.771 0.800
## [11351] 0.776 0.861 0.853 0.977 0.901 0.799 0.860 0.827 0.879 0.815
## [11361] 0.879 0.827 0.925 0.821 0.852 0.837 0.795 0.846 0.879 0.853
## [11371] 0.877 0.862 0.767 0.781 0.765 0.769 0.798 0.779 0.791 0.855
## [11381] 0.953 0.830 0.765 0.762 0.767 0.765 0.771 0.765 0.763 0.770
## [11391] 0.773 0.766 0.766 0.767 0.763 0.762 0.764 0.763 0.769 0.765
## [11401] 0.767 0.768 0.768 0.771 0.775 0.770 0.805 0.807 0.761 0.761
## [11411] 0.866 0.769 0.801 0.782 0.763 0.765 0.763 0.771 0.768 0.779
## [11421] 0.770 0.816 0.816 0.771 0.779 0.793 0.802 0.815 0.771 0.768
## [11431] 0.776 0.770 0.783 0.803 1.051 0.856 0.795 0.788 0.930 1.288
## [11441] 0.761 0.760 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [11451] 0.761 0.787 0.761 0.870 0.761 0.761 0.761 0.761 0.761 1.048
## [11461] 1.480 2.654 1.225 0.884 0.824 1.002 0.955 0.784 0.922 0.936
## [11471] 0.881 1.396 0.908 0.963 1.158 1.033 0.940 0.945 0.789 0.863
## [11481] 0.847 0.794 0.828 0.797 0.782 0.824 1.502 2.009 1.254 1.455
## [11491] 1.512 1.172 2.251 1.504 0.793 0.764 0.796 0.762 0.761 0.776
## [11501] 0.761 0.761 0.762 0.762 0.761 0.761 0.761 0.761 0.761 0.761
## [11511] 0.761 0.761 0.761 0.824 0.775 0.793 0.807 0.783 0.774 0.783
## [11521] 0.764 0.819 0.766 0.766 0.811 0.799 0.762 0.808 0.766 0.775
## [11531] 0.763 0.762 0.857 0.762 0.761 0.763 0.769 0.766 0.762 1.059
## [11541] 0.828 0.891 0.943 0.861 0.869 1.290 1.971 0.828 0.783 0.764
## [11551] 0.778 0.806 0.833 0.830 0.867 0.875 0.835 0.815 0.867 0.870
## [11561] 0.984 0.842 1.093 0.999 0.935 0.829 0.896 0.955 1.480 3.972
## [11571] 0.761 0.761 0.761 0.982 0.761 0.866 0.907 0.954 0.931 0.898
## [11581] 0.899 1.119 0.877 0.825 0.956 0.781 0.803 0.802 0.777 0.890
## [11591] 0.783 0.960 0.762 0.761 0.761 0.761 1.959 1.246 1.686 1.147
## [11601] 1.444 1.291 1.252 1.302 0.874 0.836 0.896 0.806 0.778 0.782
## [11611] 0.774 0.779 1.029 1.133 1.098 1.243 1.305 1.159 1.270 1.115
## [11621] 1.682 1.453 0.762 0.797 0.764 0.771 0.851 0.927 0.904 1.462
## [11631] 1.910 1.935 0.761 0.889 1.071 0.892 1.110 0.943 0.761 0.762
## [11641] 0.761 0.761 0.761 0.761 1.293 1.521 1.285 1.340 1.341 1.335
## [11651] 1.726 1.315 0.761 1.319 1.405 1.612 1.191 1.177 1.291 1.327
## [11661] 1.180 1.297 1.182 1.132 1.379 1.363 1.372 1.237 1.487 1.361
## [11671] 1.348 0.761 0.761 0.761 0.761 0.875 0.931 1.315 1.131 1.130
## [11681] 1.145 0.777 4.348 0.956 1.035 0.832 0.983 2.294 1.209 1.706
## [11691] 2.326 0.933 1.284 1.367 2.714 1.410 2.672 1.435 1.113 1.812
## [11701] 1.614 0.762 0.768 0.768 0.765 0.841 0.879 0.890 0.799 0.766
## [11711] 0.820 0.769 0.781 0.794 0.771 0.763 0.774 0.777 0.783 0.777
## [11721] 0.773 0.793 0.799 0.782 0.781 0.786 0.774 0.772 0.848 0.977
## [11731] 0.847 0.927 0.762 0.994 0.762 0.761 0.853 0.761 0.775 0.763
## [11741] 0.916 0.851 1.599 1.551 1.084 0.787 0.832 0.769 0.928 0.768
## [11751] 2.258 2.491 0.761 0.761 1.158 0.904 0.763 0.766 0.959 1.584
## [11761] 0.974 1.073 1.082 0.762 0.761 0.891 1.027 1.132 1.169 0.761
## [11771] 0.761 1.027 0.761 1.208 1.199 1.160 1.170 1.021 0.761 0.761
## [11781] 1.012 0.899 0.891 1.007 0.977 0.817 0.884 0.867 0.947 0.775
## [11791] 0.823 0.794 0.863 0.912 0.788 0.831 0.784 0.775 0.791 0.925
## [11801] 1.390 1.202 0.818 0.761 0.761 0.762 0.761 0.761 0.761 0.762
## [11811] 0.761 0.841 0.761 0.761 0.761 0.954 0.761 0.761 0.761 0.761
## [11821] 1.169 1.158 1.341 1.279 1.752 1.414 4.375 2.072 1.672 1.676
## [11831] 0.925 0.828 0.857 0.936 0.795 0.834 0.826 0.789 0.887 0.792
## [11841] 0.788 0.889 0.918 1.068 1.104 0.933 0.761 0.761 0.869 0.761
## [11851] 0.786 0.832 0.891 0.834 0.869 0.852 0.867 1.229 0.840 0.970
## [11861] 1.280 0.822 1.438 0.762 0.762 0.767 0.770 0.762 0.763 0.777
## [11871] 0.765 0.787 0.769 0.770 0.762 0.763 0.802 0.775 0.780 0.899
## [11881] 1.193 1.507 1.087 0.762 0.769 0.761 0.765 0.789 0.761 0.762
## [11891] 0.816 0.919 0.904 0.926 0.979 0.915 1.020 1.061 0.888 0.822
## [11901] 1.050 0.817 1.077 0.983 2.416 1.644 0.962 0.934 0.881 0.889
## [11911] 0.935 1.056 0.937 0.898 0.869 0.875 0.868 1.736 0.778 0.828
## [11921] 0.932 0.854 0.851 0.960 0.844 0.980 1.033 1.286 1.025 1.380
## [11931] 0.982 0.989 0.949 0.849 0.900 1.031 1.015 0.856 0.984 2.309
## [11941] 0.901 1.059 1.136 4.256 2.977 0.978 0.795 1.020 0.989 1.041
## [11951] 1.159 1.155 0.790 1.207 1.277 1.429 1.399 0.892 1.212 0.852
## [11961] 1.065 0.905 0.860 0.763 0.762 0.787 0.788 0.788 0.790 0.793
## [11971] 0.761 0.822 0.808 0.788 0.783 0.775 0.766 0.792 0.765 0.778
## [11981] 0.767 0.780 0.784 0.800 0.790 0.935 0.853 0.810 0.796 0.819
## [11991] 0.779 0.789 1.116 0.893 0.867 0.829 1.008 1.012 1.229 1.221
## [12001] 0.999 1.103 0.799 0.773 0.779 0.797 0.801 0.882 0.901 0.841
## [12011] 1.044 1.035 0.913 0.905 0.879 1.024 1.074 2.863 3.173 2.860
## [12021] 2.710 0.835 0.852 1.227 0.859 1.234 0.821 0.826 2.447 1.498
## [12031] 0.761 0.817 0.761 1.939 1.277 1.324 0.762 0.799 0.792 1.006
## [12041] 0.761 0.828 0.853 0.761 0.776 1.049 1.063 2.774 2.038 0.762
## [12051] 0.933 0.968 0.914 0.762 0.762 0.911 1.448 1.097 0.776 0.774
## [12061] 0.770 0.768 0.775 0.772 0.772 0.773 0.769 0.768 0.765 0.764
## [12071] 0.765 0.764 0.764 0.763 0.764 0.763 0.763 0.764 0.764 0.789
## [12081] 0.779 0.782 0.767 0.777 0.786 1.067 1.096 0.994 1.096 1.119
## [12091] 1.279 1.320 1.054 1.130 1.213 1.227 1.027 0.876 1.169 1.112
## [12101] 1.017 0.941 0.916 0.816 0.766 0.762 0.843 1.130 0.838 0.870
## [12111] 0.858 0.892 0.811 1.926 0.761 1.945 1.988 1.633 1.496 1.375
## [12121] 1.524 1.410 1.524 1.474 1.410 2.082 1.507 2.113 2.174 1.551
## [12131] 1.431 1.810 1.412 1.124 1.097 1.287 0.761 0.761 0.761 0.761
## [12141] 0.761 0.761 0.761 1.120 0.761 0.762 0.774 0.774 0.798 0.932
## [12151] 1.232 1.475 1.454 0.947 0.985 0.841 0.838 1.171 1.229 1.370
## [12161] 0.763 0.762 0.762 0.762 0.762 0.763 0.764 0.762 0.763 0.762
## [12171] 0.762 0.762 0.764 0.766 0.764 0.765 0.765 0.763 0.763 0.769
## [12181] 0.765 0.766 0.770 0.769 0.763 0.766 0.769 0.766 0.761 0.762
## [12191] 0.761 0.762 0.761 0.761 0.764 0.765 0.762 0.857 0.764 0.762
## [12201] 0.800 0.767 0.795 0.830 0.767 0.792 0.761 0.793 0.762 0.762
## [12211] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.770 0.767 0.911
## [12221] 0.815 0.907 0.792 0.857 0.762 0.807 0.771 0.770 0.763 0.785
## [12231] 0.769 0.845 0.789 0.810 0.883 0.836 0.860 0.800 0.764 0.774
## [12241] 0.781 0.768 0.763 0.956 0.813 0.846 0.827 0.784 1.048 1.142
## [12251] 1.159 0.875 0.936 0.993 0.809 0.785 0.793 0.804 0.783 0.807
## [12261] 0.858 0.822 0.809 0.856 0.922 0.941 0.836 0.970 1.009 0.890
## [12271] 0.882 0.795 0.761 0.762 0.761 0.798 0.761 0.761 0.761 0.761
## [12281] 0.761 0.761 0.761 0.761 0.761 0.881 0.761 0.764 0.768 0.766
## [12291] 0.763 0.761 0.761 0.768 0.767 0.767 0.778 0.772 0.771 0.764
## [12301] 0.765 0.776 0.766 0.762 0.761 1.601 1.415 1.111 1.231 1.371
## [12311] 3.307 2.694 1.931 1.930 0.944 0.803 0.787 0.783 0.767 0.767
## [12321] 2.112 0.816 0.950 0.761 0.998 0.762 0.774 0.763 1.515 1.567
## [12331] 0.826 1.359 1.453 0.857 1.411 1.625 1.600 1.391 0.873 1.374
## [12341] 1.874 1.684 1.581 1.239 1.140 1.173 1.355 1.095 0.761 0.871
## [12351] 0.934 1.050 1.098 1.048 1.060 0.761 0.761 0.761 0.828 0.835
## [12361] 0.975 1.064 0.761 0.781 0.761 0.802 0.801 0.900 0.894 0.867
## [12371] 0.891 0.785 1.283 2.674 1.047 1.007 1.062 1.217 1.164 2.288
## [12381] 0.761 0.761 0.762 0.769 0.766 0.878 0.957 1.034 0.834 0.826
## [12391] 0.930 0.761 0.762 0.786 0.762 0.813 0.818 0.762 0.773 0.762
## [12401] 0.817 0.936 0.958 0.874 0.822 0.793 0.789 0.886 0.918 3.084
## [12411] 4.436 0.899 0.799 0.766 0.772 1.051 1.124 0.827 1.044 0.958
## [12421] 0.794 0.790 0.838 0.838 0.796 0.827 0.840 0.837 0.790 0.844
## [12431] 0.765 0.762 0.761 0.761 0.761 0.761 0.763 0.762 0.761 0.761
## [12441] 0.761 0.767 0.792 0.761 0.761 0.761 0.761 0.762 0.762 0.761
## [12451] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.908 0.761
## [12461] 0.761 1.439 1.219 1.494 0.763 1.411 0.763 0.763 0.892 0.846
## [12471] 1.121 1.090 1.449 1.199 1.029 1.178 0.763 0.762 0.764 0.766
## [12481] 0.765 0.764 0.764 0.763 0.764 0.761 0.761 0.761 0.761 0.761
## [12491] 0.761 0.762 0.765 0.762 0.761 0.761 0.762 0.762 0.761 0.762
## [12501] 0.764 0.763 0.761 0.816 2.833 4.152 2.977 2.133 0.761 0.761
## [12511] 0.761 0.761 1.278 1.213 1.262 0.761 0.860 1.234 1.316 0.869
## [12521] 0.869 0.805 0.791 1.261 1.298 1.112 0.764 0.764 0.873 0.766
## [12531] 0.763 0.873 1.106 1.173 1.105 0.842 1.149 0.992 0.867 0.818
## [12541] 1.100 0.830 0.798 0.813 0.804 0.829 0.917 0.826 0.825 0.818
## [12551] 1.202 0.828 0.808 0.809 0.823 0.852 0.805 0.800 0.787 0.804
## [12561] 0.814 0.785 0.798 0.804 0.787 0.809 0.816 0.824 0.802 0.761
## [12571] 1.036 0.966 0.955 1.047 1.034 1.009 1.032 0.762 0.762 0.762
## [12581] 0.762 1.121 0.764 0.766 0.763 0.816 0.761 0.761 0.761 0.761
## [12591] 0.761 0.791 0.863 0.839 0.842 0.834 0.814 0.847 0.797 0.802
## [12601] 0.767 0.858 0.802 0.824 0.812 0.856 0.813 0.808 0.789 0.800
## [12611] 0.777 0.771 0.773 0.779 0.786 0.781 0.795 0.797 0.789 0.811
## [12621] 0.894 0.762 0.784 0.781 0.784 0.807 0.784 0.762 0.789 0.762
## [12631] 1.120 0.815 0.801 0.795 0.792 0.792 0.797 0.791 0.789 0.782
## [12641] 0.794 0.802 0.818 0.802 0.808 0.837 1.045 0.785 0.795 0.790
## [12651] 0.790 0.787 1.174 0.866 1.159 0.858 0.836 1.177 0.797 1.009
## [12661] 0.831 1.186 1.098 1.002 0.824 0.856 0.835 0.864 0.878 0.889
## [12671] 0.850 0.808 0.996 0.777 0.788 0.778 0.792 0.793 0.787 0.787
## [12681] 0.783 0.771 0.784 0.776 0.828 0.792 0.781 0.805 0.828 0.805
## [12691] 0.805 0.773 1.087 0.779 1.075 0.773 0.915 0.770 0.960 0.799
## [12701] 1.032 0.802 0.948 0.805 0.899 0.799 0.835 0.788 0.835 0.829
## [12711] 0.821 0.817 0.805 0.826 0.831 0.911 0.806 0.807 0.792 0.784
## [12721] 0.783 1.067 0.761 0.761 0.761 0.761 0.809 0.801 0.761 0.762
## [12731] 0.835 0.789 0.761 0.786 1.019 0.780 0.787 0.799 0.819 0.819
## [12741] 0.819 0.798 0.782 0.778 0.782 0.791 0.780 0.788 0.788 0.791
## [12751] 0.777 0.776 0.776 1.052 0.761 0.761 0.761 0.762 0.793 0.766
## [12761] 0.813 0.840 1.095 1.165 0.838 1.071 0.823 0.974 0.983 0.904
## [12771] 0.812 0.822 0.798 0.843 0.805 0.784 0.789 0.797 0.793 0.795
## [12781] 0.782 0.787 0.783 0.783 0.786 0.781 0.773 0.796 0.792 0.863
## [12791] 0.843 0.848 0.812 0.826 0.838 0.816 0.800 0.816 0.817 0.804
## [12801] 0.823 0.818 0.870 0.845 0.853 0.829 0.797 0.784 1.093 0.764
## [12811] 0.761 0.775 0.764 0.811 0.813 0.793 0.833 0.846 0.828 0.823
## [12821] 0.849 0.853 0.812 0.808 0.849 0.838 0.841 0.841 0.849 0.858
## [12831] 0.858 0.849 0.860 0.767 0.808 1.056 0.761 0.761 0.773 0.784
## [12841] 0.807 0.808 0.795 0.794 0.835 0.796 0.817 0.849 0.832 1.148
## [12851] 0.816 1.061 1.103 1.106 1.117 0.815 1.088 0.779 1.079 1.193
## [12861] 0.848 1.014 0.788 0.800 0.803 0.787 0.779 0.843 0.797 0.799
## [12871] 0.809 0.891 0.844 0.812 0.794 0.814 0.818 0.886 0.788 0.830
## [12881] 0.793 0.803 0.780 0.774 0.768 0.798 0.793 0.776 0.816 0.790
## [12891] 0.795 0.778 0.797 0.763 0.799 0.792 1.074 0.951 0.895 1.148
## [12901] 1.098 1.139 1.176 1.272 1.183 1.221 0.836 0.780 0.788 0.766
## [12911] 0.831 0.798 0.822 0.951 0.842 0.817 0.817 1.182 0.799 0.816
## [12921] 0.893 0.807 0.799 0.815 0.912 0.956 1.014 0.845 0.930 0.955
## [12931] 0.920 0.835 0.828 0.812 0.821 0.854 0.922 0.871 0.874 0.849
## [12941] 0.900 0.846 0.830 0.859 0.823 0.861 0.843 0.816 0.834 0.829
## [12951] 0.789 0.762 0.764 0.774 0.764 0.764 0.763 0.839 0.781 0.785
## [12961] 0.789 0.791 0.789 0.784 0.776 0.776 0.772 0.796 0.787 0.792
## [12971] 0.787 0.799 0.774 0.779 0.768 0.762 0.763 0.767 0.763 0.762
## [12981] 0.762 0.761 0.766 0.763 0.825 0.862 0.887 0.903 0.857 0.865
## [12991] 0.872 0.803 0.810 0.806 0.833 0.810 0.808 0.814 0.823 0.836
## [13001] 0.877 0.827 0.785 0.788 0.804 0.800 0.797 0.772 0.812 0.819
## [13011] 0.831 0.814 0.836 0.980 0.823 0.994 0.936 0.908 0.985 0.942
## [13021] 0.919 0.888 0.873 0.909 0.887 0.876 0.910 0.828 0.811 0.829
## [13031] 0.864 0.812 0.889 0.832 0.864 0.823 0.895 0.901 0.853 0.821
## [13041] 0.807 0.778 0.789 0.794 0.784 0.782 0.791 0.889 0.873 0.845
## [13051] 0.837 0.854 0.816 0.899 0.807 0.815 0.841 0.857 0.829 0.815
## [13061] 0.845 0.851 0.862 0.856 0.841 0.776 0.810 0.771 0.789 0.780
## [13071] 0.804 0.787 0.803 0.788 1.049 0.833 0.920 0.825 0.823 0.848
## [13081] 0.792 0.885 0.940 0.885 0.931 0.924 0.895 0.880 0.867 0.853
## [13091] 0.857 0.829 0.852 0.852 0.826 0.934 0.895 0.883 0.892 0.819
## [13101] 0.861 0.805 0.879 0.819 0.775 0.809 0.856 0.875 0.845 0.817
## [13111] 0.807 0.821 0.834 0.825 0.879 0.835 0.827 0.877 0.856 0.840
## [13121] 0.813 0.827 0.819 0.821 0.804 0.814 0.836 0.816 0.816 0.833
## [13131] 0.840 0.808 0.856 0.894 0.836 0.880 0.845 0.821 0.875 0.830
## [13141] 0.768 0.783 0.785 0.776 0.778 0.803 0.785 0.800 0.807 0.801
## [13151] 0.792 0.787 0.832 0.792 0.789 0.786 0.784 0.791 0.792 0.795
## [13161] 0.791 0.811 0.781 0.772 0.774 0.777 0.785 0.798 0.779 0.786
## [13171] 0.766 0.779 0.777 0.765 0.774 0.775 0.777 0.781 0.802 0.816
## [13181] 0.808 0.802 0.808 0.801 0.804 0.801 0.787 0.786 0.787 0.779
## [13191] 0.787 0.796 0.787 0.793 0.794 0.795 0.780 0.769 0.770 0.770
## [13201] 0.772 0.774 0.774 0.771 0.781 0.775 0.973 0.784 0.791 0.788
## [13211] 0.811 0.836 0.786 0.957 0.825 0.822 0.869 0.819 0.821 0.808
## [13221] 0.797 0.807 0.791 0.790 0.895 0.820 0.818 0.815 0.873 0.876
## [13231] 0.788 0.857 0.831 0.790 0.848 0.822 0.794 0.801 0.815 0.782
## [13241] 0.799 0.795 0.802 0.808 0.798 0.822 0.810 0.808 0.811 0.795
## [13251] 0.804 0.800 0.794 0.804 0.804 0.799 0.792 0.803 0.805 0.776
## [13261] 0.784 0.791 0.815 0.826 0.799 0.808 0.819 0.808 0.817 0.784
## [13271] 0.805 0.806 0.863 0.818 0.828 0.800 0.829 0.853 0.787 1.026
## [13281] 0.930 0.927 0.907 0.875 0.836 0.835 0.815 0.826 0.785 0.784
## [13291] 0.807 0.806 0.830 0.828 0.841 0.804 0.829 0.856 0.858 0.805
## [13301] 0.843 0.813 0.789 0.798 0.817 0.797 0.801 0.798 0.796 0.802
## [13311] 0.894 0.923 0.877 0.824 0.859 0.819 0.847 0.858 0.807 0.788
## [13321] 0.785 0.806 0.774 0.785 0.805 0.789 0.802 0.800 0.778 0.785
## [13331] 0.802 0.780 0.796 0.817 0.851 0.776 0.779 0.779 0.765 0.772
## [13341] 0.774 0.772 0.777 0.813 0.825 0.806 0.799 0.799 0.807 0.814
## [13351] 0.812 0.801 0.827 0.813 0.797 0.806 0.826 0.817 0.796 0.808
## [13361] 0.821 0.806 0.773 0.774 0.776 0.786 0.771 0.776 0.765 0.774
## [13371] 0.772 0.885 0.913 0.871 0.964 0.929 0.954 0.939 0.848 0.855
## [13381] 0.861 0.852 0.811 0.834 0.861 0.841 0.863 0.861 0.799 0.776
## [13391] 0.815 0.822 0.802 0.777 0.774 0.766 0.767 0.772 0.770 0.776
## [13401] 0.797 0.843 0.813 0.845 0.826 0.816 0.844 0.805 0.798 0.775
## [13411] 0.787 0.780 0.775 0.775 0.774 0.792 0.777 0.781 0.770 0.774
## [13421] 0.773 0.774 0.766 0.771 0.765 0.765 0.770 0.769 0.786 0.892
## [13431] 0.787 0.804 0.835 0.811 0.792 0.798 0.785 0.790 0.782 0.780
## [13441] 0.825 0.787 0.782 0.793 0.836 0.863 0.823 0.787 0.806 0.834
## [13451] 0.807 0.875 0.818 0.855 0.808 0.802 0.822 0.816 0.834 0.829
## [13461] 0.820 0.807 0.810 0.804 0.807 0.812 0.847 0.798 0.805 0.799
## [13471] 0.808 0.796 0.798 0.805 0.794 0.795 0.803 0.793 0.773 0.795
## [13481] 0.802 0.813 0.828 0.843 0.814 0.871 0.886 0.787 0.949 0.934
## [13491] 0.909 0.933 0.932 0.900 0.895 0.841 0.876 0.863 0.900 0.889
## [13501] 0.925 0.878 0.905 0.949 0.880 0.858 0.864 0.860 0.837 0.859
## [13511] 0.834 0.811 0.825 0.837 1.087 0.802 0.828 0.816 0.859 0.824
## [13521] 0.834 0.912 0.884 0.878 0.932 0.981 0.861 0.823 0.805 0.826
## [13531] 0.796 0.818 0.835 0.831 0.833 0.863 0.901 0.907 0.861 0.888
## [13541] 0.857 0.843 0.830 0.819 0.940 0.865 0.848 0.888 0.864 0.924
## [13551] 0.921 0.909 0.847 0.828 0.954 0.940 0.884 0.917 0.942 0.883
## [13561] 0.891 0.826 0.846 0.815 0.841 0.852 0.850 0.855 0.851 0.852
## [13571] 0.867 0.830 0.910 0.829 0.867 0.823 0.830 0.882 0.922 0.833
## [13581] 1.082 0.827 0.900 0.853 0.877 0.817 0.781 0.930 0.832 0.918
## [13591] 0.871 0.857 0.878 0.816 0.825 0.826 0.828 0.844 0.848 0.845
## [13601] 0.866 0.851 0.884 0.859 0.826 0.858 0.884 0.811 0.912 0.829
## [13611] 0.814 0.861 0.870 0.959 0.886 0.981 0.975 0.931 0.982 0.804
## [13621] 0.903 0.812 0.825 0.828 0.976 0.952 0.965 0.979 0.926 0.979
## [13631] 0.974 1.010 0.987 0.973 0.957 0.939 0.869 0.862 0.913 0.930
## [13641] 0.956 0.898 0.920 0.808 0.934 0.925 0.812 0.886 0.871 0.893
## [13651] 0.864 0.924 0.854 0.891 0.860 0.860 0.856 0.875 0.872 0.813
## [13661] 0.827 0.824 0.838 0.840 0.840 0.834 0.828 0.804 0.817 0.836
## [13671] 0.834 0.854 0.829 0.867 0.852 0.873 0.850 0.869 0.850 0.768
## [13681] 0.836 0.780 0.805 0.827 0.909 0.889 0.861 0.891 0.886 0.885
## [13691] 0.835 0.796 0.799 0.854 0.820 0.821 0.838 0.830 0.835 0.840
## [13701] 0.868 0.825 0.797 0.822 0.800 0.791 0.775 0.789 0.764 0.795
## [13711] 0.804 4.327 5.747 5.744 6.663 4.367 4.265 3.975 3.942 4.179
## [13721] 3.183 3.926 3.933 3.301 3.645 3.479 4.090 3.835 4.712 4.228
## [13731] 4.228 1.839 4.087 3.290 2.275 1.971 1.515 2.487 4.031 0.762
## [13741] 1.212 1.532 0.762 1.458 0.762 1.641 1.496 1.553 2.499 2.023
## [13751] 2.120 0.930 0.764 1.571 1.870 1.846 1.396 1.920 1.792 2.482
## [13761] 2.161 2.737 2.806 2.750 1.518 0.762 2.399 1.360 0.878 0.822
## [13771] 0.825 0.843 0.829 0.819 0.792 0.805 0.802 0.786 0.825 0.816
## [13781] 0.798 0.915 1.331 1.076 0.881 1.143 0.765 0.761 0.762 0.762
## [13791] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [13801] 0.762 0.762 0.762 0.762 0.762 0.762 0.761 0.761 0.761 0.761
## [13811] 0.761 0.761 0.761 0.761 0.761 0.763 0.763 0.760 0.815 0.999
## [13821] 0.781 0.768 0.761 1.421 1.175 1.180 1.527 0.761 0.761 1.648
## [13831] 0.973 1.190 0.762 1.545 0.762 1.973 1.066 2.005 0.844 2.066
## [13841] 0.762 2.205 0.762 1.783 0.762 1.833 0.762 1.058 0.762 0.761
## [13851] 0.841 1.646 1.387 1.808 1.365 1.840 1.656 2.108 1.221 1.788
## [13861] 1.312 1.525 0.868 0.970 0.895 0.760 0.824 0.834 0.873 1.196
## [13871] 0.867 1.157 0.989 1.950 1.113 2.569 2.296 1.503 1.123 1.458
## [13881] 0.761 1.243 1.243 1.233 1.767 1.318 1.730 1.434 1.390 1.504
## [13891] 1.269 1.705 1.352 1.506 1.799 1.729 1.531 1.673 1.266 1.027
## [13901] 0.947 0.840 1.216 1.147 1.005 1.124 1.075 1.066 1.030 0.779
## [13911] 0.777 0.781 0.779 0.790 0.793 0.785 0.775 0.772 0.779 0.765
## [13921] 0.767 0.780 0.773 0.766 0.976 0.761 0.761 0.761 0.761 0.761
## [13931] 0.762 0.761 0.762 0.762 0.762 0.760 0.761 0.966 0.760 0.766
## [13941] 0.762 0.808 0.760 0.792 0.760 0.762 0.760 0.767 0.760 0.764
## [13951] 0.760 0.760 0.760 0.761 0.761 0.762 0.761 0.761 0.761 0.761
## [13961] 0.761 0.761 0.761 0.761 0.761 0.761 0.941 0.773 0.776 0.761
## [13971] 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.762
## [13981] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [13991] 0.761 0.761 0.762 0.761 0.761 0.822 0.762 0.762 0.761 0.761
## [14001] 0.761 0.761 0.761 1.366 0.762 0.762 0.761 0.761 0.761 0.761
## [14011] 0.761 0.761 1.595 0.875 0.761 0.761 0.766 1.541 0.762 0.762
## [14021] 0.762 0.762 0.762 0.762 3.043 1.229 1.741 0.859 1.955 0.762
## [14031] 0.762 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.762 0.762
## [14041] 0.761 0.761 0.761 0.761 0.761 0.762 0.762 0.769 0.761 0.761
## [14051] 0.761 0.761 0.798 0.811 0.761 0.761 0.839 0.812 0.762 0.798
## [14061] 0.762 0.800 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [14071] 0.762 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.762 0.762
## [14081] 0.761 0.761 0.761 0.761 0.761 1.519 1.043 0.837 0.761 0.761
## [14091] 0.880 0.761 1.541 2.276 1.464 1.816 1.739 1.571 1.983 1.227
## [14101] 0.762 1.940 0.762 1.594 1.843 1.756 0.971 0.909 0.774 0.761
## [14111] 0.769 0.761 0.777 0.761 0.776 0.761 0.772 0.761 0.769 0.762
## [14121] 0.773 0.777 0.797 0.781 0.761 0.787 0.761 0.813 0.761 0.782
## [14131] 0.764 0.792 0.970 0.761 0.761 0.761 0.761 0.761 0.761 0.760
## [14141] 0.761 0.762 0.816 0.762 0.762 0.762 0.762 0.761 0.762 0.761
## [14151] 0.761 0.762 0.761 0.761 0.761 0.920 0.761 0.762 0.775 0.761
## [14161] 0.789 0.921 0.943 0.957 0.966 0.944 0.829 0.939 0.902 0.952
## [14171] 0.900 0.943 0.953 0.949 0.908 0.908 0.761 0.761 0.761 0.761
## [14181] 0.761 0.762 0.761 0.762 0.762 0.762 0.761 0.957 0.941 0.946
## [14191] 0.875 0.942 1.034 1.953 2.180 1.072 1.245 0.847 3.274 1.189
## [14201] 1.149 1.319 0.778 0.799 0.782 0.779 0.784 0.774 0.790 0.761
## [14211] 0.784 0.761 0.800 1.221 0.794 0.761 0.862 0.868 0.845 0.847
## [14221] 0.811 0.957 0.915 0.773 0.924 0.761 0.839 0.835 0.761 0.761
## [14231] 0.799 0.818 0.813 0.805 0.827 0.784 0.799 0.803 0.842 0.790
## [14241] 0.782 0.789 0.761 0.762 0.771 0.789 0.776 1.265 0.777 0.934
## [14251] 0.783 0.761 0.763 0.761 0.771 0.761 0.806 2.332 0.797 1.479
## [14261] 0.812 1.464 0.771 0.878 0.806 0.767 0.768 0.765 0.784 1.496
## [14271] 0.763 0.763 0.762 1.079 0.762 0.761 0.762 0.761 0.761 0.761
## [14281] 0.805 0.764 1.417 1.202 1.564 1.549 1.688 2.058 0.761 0.762
## [14291] 0.763 0.762 0.762 0.762 0.762 0.765 0.762 0.761 0.761 0.762
## [14301] 0.761 0.761 0.762 0.761 0.762 0.761 0.777 0.787 0.795 0.801
## [14311] 0.787 0.800 0.828 0.816 0.803 0.808 0.820 0.790 0.802 0.813
## [14321] 0.840 0.865 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.762
## [14331] 0.762 0.762 0.761 0.855 0.798 0.799 0.815 0.786 1.067 1.162
## [14341] 1.121 1.138 1.135 1.131 1.160 1.186 1.122 1.088 1.140 1.127
## [14351] 1.079 0.762 1.129 0.762 0.761 0.870 0.761 0.760 0.761 0.761
## [14361] 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.762 0.761
## [14371] 0.761 0.761 0.764 0.761 0.762 1.511 0.761 0.762 0.762 0.762
## [14381] 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.761
## [14391] 0.761 0.762 1.065 0.761 1.072 1.123 0.761 1.276 1.438 0.760
## [14401] 0.761 0.762 0.763 0.762 0.762 0.762 0.762 0.761 0.762 0.761
## [14411] 0.761 0.762 0.761 0.761 0.761 0.761 0.762 0.761 1.002 0.762
## [14421] 0.761 0.762 0.762 0.762 0.762 4.353 8.238 2.898 5.960 1.663
## [14431] 1.168 0.770 0.983 0.993 0.875 0.780 0.764 0.772 0.774 0.772
## [14441] 0.763 0.764 0.764 0.762 0.762 0.762 0.762 0.763 0.764 0.789
## [14451] 0.765 0.942 0.762 0.801 0.769 0.781 0.819 0.770 0.762 0.762
## [14461] 0.762 3.831 7.223 2.179 2.172 2.733 1.778 3.007 2.739 3.861
## [14471] 4.323 5.778 4.822 2.890 2.680 2.051 2.812 2.180 5.758 2.142
## [14481] 3.706 6.758 4.337 11.107 1.856 1.721 1.361 3.869 1.371 1.216
## [14491] 0.817 0.762 0.761 0.763 5.272 6.878 1.523 1.679 0.877 3.506
## [14501] 2.368 1.662 4.287 2.632 0.958 0.901 1.771 3.610 0.761 4.940
## [14511] 0.952 0.761 0.761 0.761 0.761 1.435 0.761 1.509 0.948 0.762
## [14521] 0.762 0.762 0.762 2.136 10.288 5.504 0.761 1.264 1.010 0.761
## [14531] 0.761 0.761 0.761 3.825 2.962 0.761 8.253 2.152 2.014 3.858
## [14541] 3.813 2.646 6.501 3.706 1.958 1.662 1.875 0.762 1.672 4.986
## [14551] 8.326 6.771 3.151 3.925 1.135 1.360 1.240 1.156 0.761 6.202
## [14561] 6.093 3.201 5.867 2.927 3.934 3.625 4.717 2.414 5.743 2.253
## [14571] 3.027 4.868 1.672 2.686 1.291 5.197 3.668 3.049 2.354 3.948
## [14581] 4.113 4.665 5.629 5.294 2.669 5.755 1.286 3.243 1.808 2.908
## [14591] 6.710 8.595 6.606 3.964 4.516 3.395 3.610 3.104 1.914 2.905
## [14601] 1.512 2.685 0.819 1.182 1.958 2.745 1.886 0.872 3.156 3.407
## [14611] 5.436 5.951 3.141 4.263 4.465 3.914 3.212 2.939 1.540 0.762
## [14621] 0.762 0.761 0.761 0.761 0.761 0.761 0.762 1.091 0.769 0.958
## [14631] 0.811 1.025 1.039 0.761 1.141 0.775 0.761 0.925 0.761 0.761
## [14641] 0.761 0.869 0.761 1.062 0.891 0.796 0.969 0.778 1.017 0.875
## [14651] 1.441 1.594 1.546 1.516 1.514 0.760 0.820 1.916 0.761 0.761
## [14661] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [14671] 0.761 1.164 2.279 0.762 2.051 0.762 0.762 0.762 0.762 0.762
## [14681] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.761 0.916 2.236
## [14691] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.761 0.761 0.761
## [14701] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [14711] 0.761 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [14721] 0.762 0.762 0.762 0.762 0.765 0.761 0.761 0.826 0.762 0.762
## [14731] 0.762 0.762 0.762 0.762 0.762 0.815 0.762 0.761 0.761 0.761
## [14741] 1.111 1.330 1.411 0.827 0.761 0.771 0.761 1.054 0.762 0.982
## [14751] 0.762 0.761 0.800 0.761 0.807 0.863 0.811 0.761 0.803 0.761
## [14761] 0.811 0.761 0.810 0.797 0.794 0.807 0.762 0.762 0.762 0.762
## [14771] 0.762 0.762 0.762 0.762 0.762 0.761 0.761 0.761 0.932 0.761
## [14781] 1.147 0.761 1.116 0.956 1.156 0.761 1.075 1.332 1.134 0.936
## [14791] 1.081 1.130 1.128 1.132 1.020 1.110 1.157 1.123 1.119 1.143
## [14801] 0.866 1.109 1.018 1.404 0.896 1.191 0.772 1.136 0.761 1.106
## [14811] 0.761 1.119 0.761 1.143 0.815 1.003 0.797 0.993 0.773 1.077
## [14821] 0.800 1.006 0.776 1.820 0.959 1.892 1.329 4.095 2.290 1.982
## [14831] 4.089 1.011 0.851 0.761 0.761 1.033 0.761 1.124 0.761 1.095
## [14841] 1.045 1.108 1.031 1.046 1.073 0.764 1.051 1.109 1.115 1.023
## [14851] 0.761 1.157 0.901 1.172 0.862 1.123 0.761 1.087 0.761 0.761
## [14861] 0.761 0.761 0.761 0.761 0.770 1.067 1.145 1.154 1.141 1.069
## [14871] 1.094 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [14881] 0.762 0.761 0.762 2.096 1.496 2.075 1.185 0.762 0.761 0.761
## [14891] 0.761 0.761 0.761 0.761 0.848 0.761 0.761 0.761 0.761 1.030
## [14901] 3.820 3.376 2.338 1.185 0.761 0.761 0.761 0.761 0.761 0.761
## [14911] 0.761 0.761 0.761 0.761 0.761 0.761 1.022 2.371 3.153 2.001
## [14921] 3.050 2.861 1.892 0.997 0.781 1.035 1.517 0.760 0.762 0.761
## [14931] 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.762
## [14941] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.762
## [14951] 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.761 0.761
## [14961] 0.761 0.762 0.761 0.762 0.760 0.760 0.761 0.760 0.762 0.761
## [14971] 0.760 0.762 0.761 0.760 0.761 0.761 0.760 0.762 0.760 0.761
## [14981] 0.762 0.760 0.761 0.760 0.761 0.761 0.760 0.761 0.762 0.760
## [14991] 0.761 1.133 0.760 0.760 0.761 0.761 0.761 0.761 0.761 0.760
## [15001] 0.761 0.761 0.761 0.761 0.760 0.760 0.760 0.761 0.761 0.761
## [15011] 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.763
## [15021] 0.761 0.761 0.761 0.761 0.760 0.763 0.761 0.761 0.763 0.762
## [15031] 0.761 0.763 0.761 0.762 0.766 0.761 0.766 0.761 0.766 0.761
## [15041] 1.832 0.761 1.336 0.761 0.761 0.761 0.766 0.761 0.761 0.761
## [15051] 0.762 0.761 0.762 0.762 0.762 0.761 0.761 0.760 2.000 0.761
## [15061] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [15071] 0.761 1.313 0.824 0.760 0.844 0.761 0.761 2.546 0.762 3.286
## [15081] 0.762 0.761 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761
## [15091] 0.760 0.761 1.375 0.760 0.762 1.375 0.760 0.761 0.761 0.760
## [15101] 0.761 0.763 0.760 0.760 0.761 0.761 1.083 0.761 0.761 0.761
## [15111] 0.761 0.760 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.762
## [15121] 0.761 0.761 0.762 0.763 0.761 0.761 0.761 0.766 0.762 2.777
## [15131] 1.312 3.636 0.938 0.911 0.913 0.945 0.922 0.976 0.906 0.822
## [15141] 0.787 0.867 0.871 0.980 0.912 0.907 0.864 0.918 0.912 0.887
## [15151] 0.969 0.760 0.760 0.760 0.760 0.760 0.760 0.760 0.760 1.491
## [15161] 0.840 1.006 0.811 0.972 0.926 0.870 0.761 0.903 0.761 0.867
## [15171] 0.762 0.928 0.761 0.925 0.760 0.911 0.762 0.905 0.762 0.940
## [15181] 0.762 0.894 0.761 0.951 0.761 0.951 0.761 0.908 0.761 0.970
## [15191] 0.761 0.906 0.761 0.860 0.761 0.909 0.761 0.933 0.761 0.928
## [15201] 0.766 0.974 0.998 0.845 0.938 0.967 0.952 0.968 1.522 2.140
## [15211] 1.287 1.938 0.762 0.761 0.761 0.762 0.761 0.760 0.762 0.761
## [15221] 0.761 0.761 0.761 0.952 0.925 0.967 0.943 0.927 0.856 0.867
## [15231] 0.953 0.884 0.761 0.923 0.888 0.862 0.827 0.935 0.857 0.916
## [15241] 0.889 0.925 0.925 0.915 0.952 0.880 0.917 0.815 0.882 0.916
## [15251] 0.772 0.835 0.931 0.845 0.807 0.926 0.949 0.801 0.783 0.926
## [15261] 0.938 0.945 0.777 0.879 0.931 0.777 0.761 0.905 0.763 0.762
## [15271] 0.763 0.846 0.761 0.761 0.925 0.809 0.835 0.761 0.925 0.760
## [15281] 0.760 0.784 0.763 0.760 0.762 0.761 0.763 0.760 0.762 0.761
## [15291] 0.763 0.760 0.764 0.774 0.763 0.760 0.761 0.800 0.782 0.760
## [15301] 0.876 0.770 0.797 0.760 1.809 0.763 0.760 0.837 0.761 0.760
## [15311] 0.761 0.767 1.237 0.773 0.761 0.760 0.761 0.841 1.284 0.780
## [15321] 1.307 0.829 1.373 0.782 0.798 0.798 0.761 0.805 0.760 0.761
## [15331] 0.895 0.833 0.761 1.150 0.765 0.761 0.761 0.773 0.935 0.761
## [15341] 0.762 0.761 0.807 0.834 0.797 0.761 0.762 0.832 0.762 0.762
## [15351] 0.762 0.761 0.839 0.761 0.780 0.762 0.760 0.867 0.772 0.764
## [15361] 0.762 0.762 0.896 0.770 0.807 0.762 0.762 0.788 0.762 0.761
## [15371] 0.762 0.762 0.807 0.761 0.828 0.761 0.818 0.763 0.878 0.761
## [15381] 0.804 0.762 0.910 0.761 0.879 0.761 0.933 0.761 0.820 0.761
## [15391] 0.922 0.761 0.768 0.782 0.846 0.761 0.852 0.811 0.958 0.761
## [15401] 0.928 0.761 0.971 0.761 0.825 0.781 0.921 0.761 0.870 0.761
## [15411] 0.893 0.766 0.824 0.780 0.935 0.924 0.913 0.856 0.859 0.940
## [15421] 0.933 0.846 0.884 0.950 0.873 0.791 0.926 0.802 0.761 0.764
## [15431] 0.762 0.762 0.782 0.761 1.376 0.782 0.792 0.794 0.865 0.918
## [15441] 0.844 0.760 0.760 0.937 0.760 0.931 0.940 0.760 0.954 0.866
## [15451] 0.760 0.800 0.866 0.866 1.095 1.525 0.831 0.778 0.760 1.220
## [15461] 0.762 1.009 0.798 0.762 0.846 0.889 0.762 0.830 0.908 0.761
## [15471] 0.761 1.380 0.814 0.761 0.829 0.761 2.053 0.864 0.761 1.342
## [15481] 0.873 0.761 1.474 0.800 0.761 1.411 0.815 0.761 1.314 0.761
## [15491] 0.766 0.940 0.867 0.906 0.927 0.898 0.914 0.930 0.902 0.920
## [15501] 0.929 0.883 0.868 0.918 0.889 0.926 0.894 0.942 0.942 0.760
## [15511] 0.760 0.958 0.762 0.761 0.761 0.761 0.766 0.932 0.903 0.919
## [15521] 2.797 4.270 6.197 3.766 3.937 3.400 2.105 2.117 1.982 1.912
## [15531] 1.826 1.208 1.951 2.383 1.239 2.418 1.520 2.177 1.982 2.304
## [15541] 1.230 1.271 1.335 1.292 1.886 0.762 3.224 2.928 3.258 3.273
## [15551] 2.824 1.145 0.915 1.060 1.218 2.260 2.377 0.761 4.008 0.761
## [15561] 7.483 0.761 10.194 0.761 7.092 1.507 8.171 1.199 7.961 2.512
## [15571] 8.048 0.761 6.367 0.761 5.661 5.727 2.974 3.568 3.919 6.383
## [15581] 2.240 0.761 0.761 0.761 0.761 0.761 0.761 0.762 1.152 1.060
## [15591] 1.068 1.051 1.082 1.097 1.029 1.085 1.006 1.007 1.018 0.926
## [15601] 0.938 1.821 1.018 0.968 1.815 1.786 1.668 2.364 1.704 0.761
## [15611] 0.761 0.761 0.761 0.761 0.761 0.761 1.224 1.168 1.219 0.896
## [15621] 1.254 1.276 1.243 2.690 0.761 1.191 2.251 1.835 1.288 3.220
## [15631] 1.284 1.211 1.327 1.020 2.057 1.563 1.211 0.969 2.515 2.494
## [15641] 1.193 0.888 6.026 1.728 0.911 1.011 5.446 2.549 1.036 0.983
## [15651] 1.534 2.477 0.908 0.888 2.150 0.875 1.335 1.298 1.834 0.925
## [15661] 0.891 1.226 2.661 1.018 0.950 1.680 2.407 1.349 0.957 1.235
## [15671] 1.896 0.867 1.050 1.142 1.633 0.997 0.963 2.424 1.617 0.937
## [15681] 0.869 1.941 1.088 0.881 0.871 1.052 1.605 0.999 1.176 1.236
## [15691] 1.666 1.001 0.858 1.499 1.906 0.851 0.875 1.193 1.594 1.867
## [15701] 0.875 2.387 1.078 0.924 0.874 2.354 1.032 0.843 0.858 1.926
## [15711] 1.834 0.961 0.866 2.310 1.862 0.950 0.870 1.859 1.793 1.590
## [15721] 1.915 3.120 2.501 2.035 1.899 2.789 1.369 2.249 2.491 3.089
## [15731] 0.762 1.475 0.766 3.157 1.990 1.840 1.938 2.064 1.344 1.940
## [15741] 1.523 1.108 3.729 1.977 0.982 2.219 1.608 1.497 0.995 0.761
## [15751] 1.883 0.933 1.292 0.898 1.047 1.640 3.160 1.309 1.058 0.761
## [15761] 2.179 1.907 1.856 3.524 1.191 0.965 1.539 5.260 2.735 0.966
## [15771] 1.481 5.987 2.045 1.519 1.528 5.331 2.187 0.911 1.430 6.991
## [15781] 1.517 0.847 0.936 5.009 2.530 1.099 0.940 4.542 1.530 0.761
## [15791] 0.947 2.980 2.813 0.865 0.951 1.615 2.493 0.827 0.917 1.528
## [15801] 1.641 1.172 0.966 1.505 2.162 0.820 0.879 1.534 1.283 0.764
## [15811] 1.190 1.530 1.904 1.062 0.953 0.873 1.343 1.406 0.761 1.072
## [15821] 0.761 0.941 2.087 1.056 0.761 0.982 0.761 1.026 1.182 1.019
## [15831] 0.761 1.002 0.761 1.145 0.761 1.034 0.761 0.912 0.761 1.036
## [15841] 1.238 1.575 0.761 0.939 1.849 0.906 2.477 1.530 1.195 1.054
## [15851] 0.762 1.039 1.228 1.072 1.232 1.337 1.316 1.380 2.840 2.536
## [15861] 2.294 3.693 3.082 3.394 2.465 2.180 3.043 3.703 3.192 3.673
## [15871] 3.130 3.101 3.422 2.900 3.472 3.299 2.863 3.122 2.281 2.662
## [15881] 2.926 2.280 1.575 0.761 1.909 1.971 3.102 2.304 1.248 6.410
## [15891] 1.634 1.001 2.913 2.238 1.072 3.631 1.277 0.973 0.761 1.432
## [15901] 0.979 3.439 0.990 0.950 3.186 1.295 0.872 3.703 1.278 0.761
## [15911] 4.658 1.102 0.991 5.929 1.139 0.973 3.439 1.286 2.347 1.011
## [15921] 0.994 0.846 1.498 1.030 1.241 1.128 2.731 0.764 1.431 4.574
## [15931] 1.742 1.970 1.520 2.832 1.721 5.992 1.477 1.496 0.761 1.496
## [15941] 1.522 1.496 0.762 0.761 1.618 0.761 0.761 1.218 0.761 1.623
## [15951] 1.060 2.267 1.456 1.564 1.242 1.251 0.996 1.655 1.977 3.533
## [15961] 2.102 2.501 2.856 2.883 2.716 2.779 2.438 2.111 2.869 2.911
## [15971] 1.587 1.412 2.269 1.398 1.484 3.349 3.366 2.444 0.761 1.902
## [15981] 1.481 1.525 1.341 2.239 2.001 0.761 1.516 1.496 0.761 0.761
## [15991] 0.761 0.845 0.987 0.761 0.896 1.007 0.761 0.761 1.676 0.761
## [16001] 0.892 1.010 0.761 0.908 1.029 0.762 0.761 1.329 1.181 0.761
## [16011] 1.070 1.029 0.761 0.992 0.791 0.761 1.073 0.999 0.761 0.978
## [16021] 1.042 0.761 2.994 1.082 0.761 1.862 1.012 0.763 2.735 0.835
## [16031] 0.761 2.434 1.173 0.761 2.538 0.820 0.761 0.975 0.851 0.761
## [16041] 3.063 0.761 0.761 0.822 0.805 0.761 2.024 0.761 0.798 0.761
## [16051] 2.292 0.831 0.761 0.761 2.982 0.815 0.762 0.922 3.153 0.761
## [16061] 0.961 0.761 1.118 3.020 0.848 1.449 0.948 1.380 0.761 1.163
## [16071] 6.871 1.687 1.574 0.851 1.401 1.626 1.278 3.692 1.785 3.801
## [16081] 2.259 0.877 1.919 1.044 0.904 2.386 2.142 1.247 1.181 3.859
## [16091] 0.962 0.997 0.805 0.761 0.944 0.802 0.761 1.036 0.810 0.761
## [16101] 1.161 0.826 0.761 1.184 0.908 0.761 1.085 1.182 0.761 1.036
## [16111] 1.675 0.761 1.058 0.882 0.940 0.765 0.932 0.761 0.790 0.822
## [16121] 0.761 0.870 0.761 0.911 0.833 0.815 0.888 0.849 0.809 0.995
## [16131] 0.778 0.868 0.761 0.761 1.142 0.761 0.988 1.110 0.761 0.761
## [16141] 0.983 0.761 0.761 0.817 0.762 0.762 0.876 0.793 0.775 0.801
## [16151] 0.891 0.887 0.777 0.801 0.848 0.803 0.805 0.765 0.768 0.769
## [16161] 0.770 0.769 0.773 0.768 0.777 0.804 0.842 0.839 0.776 0.764
## [16171] 0.763 0.767 0.768 0.769 0.769 0.768 0.772 0.766 0.766 0.765
## [16181] 0.764 0.764 0.764 0.765 0.776 0.767 0.774 0.763 0.765 0.765
## [16191] 0.763 0.777 0.765 0.765 0.766 0.764 0.765 0.765 0.786 0.762
## [16201] 0.764 0.763 0.763 0.767 0.763 0.763 0.763 0.763 0.764 0.763
## [16211] 0.763 0.768 0.764 0.763 0.762 0.762 0.771 0.783 0.809 0.854
## [16221] 0.881 0.895 0.899 0.930 0.807 0.805 0.773 0.775 0.812 0.805
## [16231] 0.795 0.790 0.833 0.819 0.807 0.761 0.761 0.761 0.761 0.761
## [16241] 0.793 0.790 0.766 0.766 0.776 0.824 0.779 0.771 0.768 0.762
## [16251] 0.762 0.764 0.777 0.773 0.772 0.766 0.769 0.765 0.770 0.765
## [16261] 0.774 0.774 0.764 0.763 0.790 0.794 0.761 0.774 0.990 0.847
## [16271] 1.051 1.028 0.955 1.037 1.011 1.001 1.111 1.429 1.280 1.402
## [16281] 1.174 1.101 1.371 1.426 1.279 1.655 1.179 1.439 3.014 0.771
## [16291] 0.763 0.765 0.767 0.765 0.768 0.794 0.833 0.826 0.881 0.869
## [16301] 0.846 0.831 0.869 0.784 0.783 0.789 0.786 0.779 0.800 0.794
## [16311] 0.823 0.855 0.761 0.761 0.761 0.761 0.763 0.845 0.904 0.927
## [16321] 0.763 0.762 0.762 0.822 0.823 0.783 0.868 0.858 0.761 0.844
## [16331] 0.790 0.955 0.777 0.894 0.807 0.890 0.862 1.079 0.761 0.761
## [16341] 0.761 0.800 0.762 0.761 0.762 0.764 0.765 0.762 0.762 0.818
## [16351] 0.882 0.914 0.892 0.888 0.900 0.793 0.786 0.768 0.767 0.771
## [16361] 0.762 0.770 0.766 0.770 0.765 0.766 0.770 0.765 0.765 0.900
## [16371] 0.881 0.761 0.761 0.851 0.818 0.849 0.782 0.813 0.854 0.854
## [16381] 0.853 0.882 0.808 0.778 0.763 0.761 0.762 0.820 0.826 0.870
## [16391] 0.871 0.926 0.919 0.928 0.891 0.772 0.816 0.761 0.761 0.796
## [16401] 0.790 0.825 0.834 0.831 0.871 0.821 0.827 0.852 0.986 0.938
## [16411] 0.795 0.888 0.833 0.831 0.867 1.120 0.820 0.881 0.866 0.845
## [16421] 0.841 0.832 0.845 0.858 0.859 0.819 0.842 0.838 0.826 0.852
## [16431] 0.846 0.841 0.855 0.883 0.848 0.906 0.880 0.871 0.876 0.895
## [16441] 0.847 0.919 1.017 0.829 0.814 0.897 0.842 0.774 0.814 0.803
## [16451] 0.839 0.922 0.913 0.951 0.977 0.943 0.948 0.859 1.063 1.027
## [16461] 1.114 0.833 0.905 0.948 1.006 0.826 1.003 1.032 0.975 1.060
## [16471] 0.944 1.019 0.921 0.937 0.902 0.906 0.912 0.953 0.917 0.936
## [16481] 0.957 0.936 0.925 0.944 0.762 0.857 0.916 0.761 0.891 0.911
## [16491] 0.843 0.923 1.000 0.902 0.999 1.065 1.094 0.969 1.064 0.910
## [16501] 0.928 1.018 0.912 1.017 1.060 0.904 1.042 0.999 1.002 1.060
## [16511] 1.124 0.942 1.016 1.199 0.927 1.086 1.054 1.073 1.121 1.085
## [16521] 0.968 0.945 1.076 0.977 1.050 1.036 0.988 1.078 1.086 1.048
## [16531] 1.175 1.064 1.055 1.092 1.326 1.057 1.060 1.171 1.306 1.065
## [16541] 1.251 1.079 1.005 1.327 1.067 1.021 0.971 1.089 1.123 0.925
## [16551] 0.931 0.775 0.964 1.172 1.002 1.005 1.172 1.167 1.518 1.441
## [16561] 1.106 1.079 1.041 1.085 1.105 1.012 1.008 1.024 1.009 1.007
## [16571] 1.074 1.010 1.083 1.040 1.003 1.093 0.761 0.761 0.761 0.761
## [16581] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [16591] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [16601] 0.762 0.863 0.761 0.868 0.761 0.760 0.761 0.761 0.760 0.761
## [16611] 0.831 0.840 0.761 0.825 0.761 0.854 0.761 0.860 0.761 0.847
## [16621] 0.761 0.831 0.761 0.828 0.761 0.838 0.761 0.838 0.761 1.510
## [16631] 0.761 1.299 0.761 1.536 0.761 1.808 0.761 1.460 1.552 1.554
## [16641] 0.761 1.537 1.556 1.563 1.566 1.592 1.610 0.761 1.536 1.600
## [16651] 1.575 1.506 1.537 1.556 1.585 1.554 1.574 1.600 1.556 1.568
## [16661] 1.206 0.994 0.761 1.010 0.761 0.899 0.872 0.880 0.811 0.883
## [16671] 0.889 0.761 0.868 1.035 0.831 0.901 0.854 0.796 0.834 0.761
## [16681] 0.855 0.761 0.885 0.761 0.839 1.007 0.838 0.946 0.844 0.761
## [16691] 0.818 0.778 0.801 0.885 0.972 0.944 0.951 0.972 0.975 0.951
## [16701] 0.899 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [16711] 0.761 0.761 0.761 0.763 0.761 0.761 0.761 0.761 0.761 0.761
## [16721] 0.761 0.761 0.761 0.761 0.770 0.766 0.761 0.761 0.761 0.761
## [16731] 0.761 0.761 0.761 0.762 0.761 0.762 0.761 0.762 0.761 0.762
## [16741] 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.762 0.761 0.762
## [16751] 0.761 0.762 0.761 0.761 0.761 0.761 0.763 0.762 0.761 0.761
## [16761] 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762
## [16771] 0.761 0.762 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.762
## [16781] 0.762 0.762 0.762 0.762 0.762 0.761 0.762 0.762 0.761 0.761
## [16791] 0.761 0.761 0.761 0.824 0.761 0.781 0.762 0.762 0.761 0.762
## [16801] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.762
## [16811] 0.761 0.920 0.843 0.867 0.783 0.915 0.799 0.782 0.811 0.860
## [16821] 0.842 0.915 0.837 0.809 0.843 0.855 0.791 0.967 0.906 0.867
## [16831] 1.809 0.907 1.584 0.806 1.673 0.788 1.747 0.867 1.761 0.868
## [16841] 1.754 0.921 1.838 0.941 1.845 0.936 1.794 0.953 1.721 0.975
## [16851] 1.713 1.041 1.967 0.898 1.914 0.933 2.005 0.871 1.635 0.991
## [16861] 1.265 1.902 1.628 1.606 1.462 1.206 1.714 1.901 1.623 1.638
## [16871] 1.514 0.981 0.907 0.860 0.986 0.842 0.888 0.786 0.845 0.785
## [16881] 0.910 0.784 0.951 0.831 0.842 0.861 0.921 0.880 1.084 0.911
## [16891] 0.903 0.896 0.909 0.771 0.954 0.816 0.985 0.830 0.903 0.800
## [16901] 0.899 0.761 0.761 0.861 0.865 0.761 0.761 1.922 1.811 1.437
## [16911] 1.942 1.713 2.046 2.167 1.646 2.132 1.539 2.442 2.140 2.281
## [16921] 2.184 1.103 2.361 2.280 2.179 2.039 1.840 2.814 2.307 2.654
## [16931] 1.861 3.452 2.322 3.316 2.201 3.653 2.260 3.723 2.778 4.404
## [16941] 2.534 5.732 0.907 2.261 2.365 2.593 2.678 2.650 3.306 1.907
## [16951] 4.226 1.762 3.440 2.742 3.663 6.081 2.802 4.764 1.744 1.959
## [16961] 3.817 3.287 5.968 5.266 2.224 2.787 4.480 7.055 5.270 5.713
## [16971] 5.303 3.681 4.717 4.598 2.848 2.966 3.109 3.156 2.149 2.399
## [16981] 2.170 2.131 1.948 1.754 1.414 2.439 1.116 1.668 1.551 1.701
## [16991] 0.800 1.809 2.239 1.639 2.078 2.055 1.990 2.532 2.691 1.126
## [17001] 2.643 2.512 2.457 3.262 2.176 2.457 2.831 1.820 1.738 1.540
## [17011] 1.628 1.777 1.792 1.533 0.762 0.762 0.762 0.762 0.762 0.762
## [17021] 0.762 0.762 0.762 0.813 1.084 1.168 1.588 1.493 1.335 1.374
## [17031] 1.344 1.587 1.212 1.523 0.761 0.762 1.528 0.761 1.294 1.989
## [17041] 2.813 1.344 1.628 1.572 1.484 2.521 5.745 5.690 2.718 5.975
## [17051] 8.509 7.192 5.954 1.774 2.408 1.490 1.702 1.012 1.149 1.263
## [17061] 0.942 1.660 1.884 2.303 1.694 1.190 2.081 2.249 2.181 2.177
## [17071] 2.069 2.241 1.970 1.962 2.166 1.979 2.315 2.551 1.015 2.226
## [17081] 2.924 3.231 3.726 2.565 4.009 3.730 1.693 2.471 1.030 2.132
## [17091] 2.102 2.178 1.635 0.924 1.605 1.684 1.823 1.601 2.066 1.809
## [17101] 1.593 2.401 1.373 2.106 2.309 2.154 2.369 1.878 2.413 2.318
## [17111] 2.137 1.941 2.042 3.060 2.166 3.072 2.008 3.622 2.164 3.535
## [17121] 2.312 3.368 2.348 3.537 2.827 4.070 2.231 2.893 1.108 1.854
## [17131] 2.252 3.087 2.584 3.095 2.072 4.020 4.407 4.442 4.619 3.443
## [17141] 7.920 9.410 1.980 2.305 3.841 2.924 3.970 3.106 6.181 5.258
## [17151] 1.706 3.770 8.613 3.881 2.934 2.011 3.122 1.884 2.828 4.406
## [17161] 1.542 2.369 2.604 2.510 1.738 1.720 2.178 1.798 1.648 1.723
## [17171] 2.037 1.300 1.631 1.519 1.666 2.058 2.074 2.160 1.704 2.075
## [17181] 1.978 2.756 2.973 3.319 3.690 3.336 3.755 2.869 2.280 1.587
## [17191] 3.990 5.722 3.290 5.035 3.311 5.308 2.129 2.087 0.891 1.002
## [17201] 0.902 1.227 1.082 1.313 1.097 1.763 2.243 1.564 0.973 1.658
## [17211] 0.826 1.983 0.842 1.984 0.826 2.127 1.273 1.710 1.633 1.804
## [17221] 2.633 2.665 0.762 0.761 1.393 0.761 1.243 1.392 2.179 2.992
## [17231] 3.018 2.211 1.843 5.192 1.953 3.112 1.884 2.699 1.334 1.273
## [17241] 9.000 8.350 2.638 3.236 2.681 4.854 3.304 1.740 3.152 2.870
## [17251] 1.371 1.574 1.450 0.999 1.465 1.634 1.351 1.290 1.263 1.020
## [17261] 1.028 2.058 7.328 0.762 0.762 0.762 0.762 0.761 0.762 0.762
## [17271] 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761
## [17281] 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762
## [17291] 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762
## [17301] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [17311] 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762
## [17321] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [17331] 0.761 0.762 0.762 0.762 1.358 0.762 0.762 0.762 0.761 0.762
## [17341] 0.762 0.762 1.540 0.762 0.762 0.762 1.228 0.762 0.762 0.762
## [17351] 1.345 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762
## [17361] 0.762 0.762 1.361 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [17371] 1.501 0.762 0.762 1.110 2.193 0.762 0.762 1.397 1.163 0.761
## [17381] 0.762 2.184 1.383 0.762 0.762 2.098 2.215 0.761 1.035 0.762
## [17391] 1.308 0.762 1.438 2.111 1.716 1.639 0.761 1.471 1.485 0.761
## [17401] 1.376 1.920 0.762 0.761 0.762 0.762 1.481 0.762 1.520 0.762
## [17411] 0.762 2.273 0.762 0.762 0.762 0.762 1.292 0.762 1.439 0.762
## [17421] 1.473 1.153 0.762 0.762 1.202 0.858 0.762 0.762 1.095 0.762
## [17431] 0.762 0.762 1.462 0.762 0.762 0.762 1.390 0.762 0.762 0.762
## [17441] 1.525 0.762 0.762 0.762 1.941 0.762 0.762 0.762 0.916 0.762
## [17451] 0.762 0.762 1.017 0.762 0.762 0.762 1.453 0.762 1.471 0.762
## [17461] 0.761 0.762 1.445 1.458 1.230 0.762 1.278 0.762 0.761 0.762
## [17471] 0.762 0.762 0.761 0.762 0.762 0.761 1.517 0.761 0.761 0.762
## [17481] 0.761 0.761 0.762 1.516 0.761 1.211 0.762 1.367 0.762 0.761
## [17491] 1.100 1.512 0.761 1.392 0.761 0.761 0.957 0.762 0.761 1.528
## [17501] 1.198 1.171 0.761 0.762 1.285 0.819 0.761 0.762 0.999 1.051
## [17511] 1.527 0.762 1.527 0.762 0.761 1.311 0.761 0.762 0.761 0.761
## [17521] 1.078 1.062 0.761 0.761 1.363 1.524 0.761 0.761 1.229 1.522
## [17531] 0.761 1.524 0.761 0.824 1.329 1.510 0.761 0.761 0.762 0.762
## [17541] 1.431 0.761 0.762 0.762 1.636 1.491 0.762 1.507 0.762 1.017
## [17551] 1.094 0.761 0.762 1.115 0.762 1.267 1.516 1.944 1.505 0.762
## [17561] 0.762 1.517 0.761 0.762 0.762 1.524 0.761 1.268 0.762 0.762
## [17571] 0.761 0.762 0.762 0.762 0.761 1.513 0.762 1.523 1.446 0.762
## [17581] 0.762 2.131 0.761 0.762 0.762 1.500 0.762 0.762 1.169 0.761
## [17591] 1.090 0.762 1.256 0.904 0.762 1.523 1.517 0.761 0.761 0.761
## [17601] 0.761 0.761 0.761 1.202 0.761 0.761 1.061 1.321 1.393 0.761
## [17611] 0.871 0.761 1.272 1.466 0.761 1.376 2.139 1.287 1.806 0.761
## [17621] 0.761 1.432 2.278 0.761 1.341 2.942 0.762 0.761 1.497 2.283
## [17631] 0.761 1.527 1.028 1.920 1.416 1.985 0.762 0.761 0.761 1.209
## [17641] 1.517 0.761 0.761 1.131 0.762 0.761 0.761 0.761 0.761 0.761
## [17651] 0.762 1.517 1.509 0.761 0.761 0.762 0.761 1.518 1.082 0.761
## [17661] 0.762 1.751 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.762
## [17671] 1.324 0.762 1.835 0.762 0.762 0.762 0.762 0.762 1.483 0.762
## [17681] 0.761 0.762 0.761 0.762 1.469 0.762 1.517 0.762 1.163 0.949
## [17691] 0.761 0.888 1.483 0.762 0.762 0.762 1.476 0.762 1.606 0.762
## [17701] 1.663 1.528 0.761 1.114 1.754 0.762 1.527 0.936 2.255 0.761
## [17711] 1.987 0.761 3.135 1.144 1.931 1.272 0.761 0.762 1.339 0.762
## [17721] 0.886 1.487 2.576 1.425 1.902 1.395 2.239 0.762 0.761 2.182
## [17731] 1.434 1.950 2.313 0.762 1.411 2.120 0.761 1.802 1.528 0.762
## [17741] 0.761 0.761 1.238 0.761 0.761 0.761 0.761 0.761 0.761 0.762
## [17751] 1.351 0.761 0.762 0.762 0.762 0.761 0.762 1.414 0.761 0.762
## [17761] 1.518 0.761 0.761 0.762 0.762 0.761 0.761 0.761 0.762 0.761
## [17771] 0.761 1.172 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.762
## [17781] 1.242 0.761 1.285 1.774 0.762 0.761 0.761 1.154 0.762 0.761
## [17791] 0.761 0.762 0.762 1.526 0.761 0.762 0.762 1.466 0.761 1.095
## [17801] 0.762 0.761 1.345 0.762 0.761 1.756 0.761 0.762 0.762 1.193
## [17811] 1.864 0.762 1.501 1.071 0.761 0.958 1.438 1.370 1.734 0.762
## [17821] 0.762 1.526 2.419 0.762 0.762 1.509 2.256 0.762 0.761 0.762
## [17831] 1.313 1.311 0.762 0.762 1.828 0.761 0.762 1.527 0.978 1.396
## [17841] 0.762 1.489 2.019 1.552 0.762 1.895 1.807 0.762 2.140 1.518
## [17851] 1.500 0.976 0.761 0.762 0.762 0.761 1.506 0.761 1.846 0.761
## [17861] 0.761 1.499 1.781 0.761 2.106 0.761 0.761 0.906 1.953 1.524
## [17871] 0.761 1.492 1.086 0.761 0.761 1.834 0.761 2.236 2.171 0.761
## [17881] 0.761 1.325 2.256 1.327 1.913 0.761 0.761 1.506 0.761 0.761
## [17891] 1.447 1.850 0.761 0.761 1.458 3.027 0.762 1.485 2.218 1.419
## [17901] 1.249 2.290 2.032 0.761 1.527 2.181 0.761 1.479 1.522 0.762
## [17911] 0.761 0.761 1.878 1.896 0.761 1.051 1.198 1.484 0.761 0.761
## [17921] 0.761 0.761 0.762 0.761 0.762 0.762 0.761 0.762 0.761 0.761
## [17931] 0.762 0.761 0.762 0.762 0.761 0.762 1.510 0.761 0.762 0.761
## [17941] 0.761 0.762 1.410 1.468 0.761 0.762 1.041 0.762 0.761 0.762
## [17951] 0.761 0.762 0.761 0.762 1.198 0.762 0.761 0.762 0.761 0.762
## [17961] 0.761 0.762 1.374 0.762 0.761 0.762 0.761 0.762 0.761 1.426
## [17971] 0.761 1.077 0.761 0.761 1.424 0.761 0.761 1.522 0.902 0.762
## [17981] 0.762 0.761 2.384 1.389 0.762 0.761 0.762 0.762 1.190 0.761
## [17991] 0.762 0.762 0.762 1.810 0.762 1.180 0.762 1.088 1.429 0.761
## [18001] 0.762 0.761 1.526 1.405 0.762 1.524 1.232 0.762 1.602 1.523
## [18011] 1.374 0.762 2.134 0.762 0.761 0.762 0.762 0.762 0.761 1.402
## [18021] 0.762 0.762 1.457 0.761 1.160 2.279 0.762 1.519 1.941 0.905
## [18031] 0.895 1.424 1.518 1.286 0.762 1.505 1.532 2.400 2.278 1.476
## [18041] 0.762 0.761 0.762 1.527 1.325 1.988 1.392 0.761 2.274 0.761
## [18051] 1.343 0.761 2.705 0.761 1.944 1.516 1.047 2.351 1.463 1.176
## [18061] 1.321 0.761 0.845 1.522 1.449 0.761 1.523 1.467 1.425 1.475
## [18071] 1.123 2.342 1.510 1.373 1.958 0.761 1.438 1.311 1.017 1.528
## [18081] 0.945 1.511 1.886 1.411 1.367 0.762 1.028 0.761 0.762 1.437
## [18091] 2.794 1.341 0.762 0.762 0.761 1.260 0.762 0.762 2.263 0.761
## [18101] 0.761 1.496 1.492 0.761 1.374 1.726 0.762 0.761 0.761 0.762
## [18111] 0.761 1.350 0.761 0.761 1.521 0.761 1.517 0.761 0.762 0.761
## [18121] 0.762 0.761 1.284 0.761 0.761 0.933 0.761 0.761 1.180 0.761
## [18131] 0.761 0.762 0.761 0.761 1.065 0.761 0.761 1.496 1.411 0.761
## [18141] 0.761 0.826 0.761 0.761 1.097 0.761 0.761 0.761 0.762 0.761
## [18151] 0.761 0.761 0.762 0.761 0.761 1.278 0.762 0.761 0.761 1.260
## [18161] 0.762 0.761 0.761 1.507 0.762 0.761 0.761 1.517 0.762 0.761
## [18171] 0.761 1.104 0.762 0.761 0.761 0.762 0.761 0.761 0.910 0.762
## [18181] 0.761 0.761 0.761 1.446 1.465 0.761 1.407 0.762 0.761 1.763
## [18191] 0.761 0.762 0.761 1.680 2.169 0.762 0.761 0.762 2.056 1.411
## [18201] 0.761 0.762 1.637 0.761 0.761 1.527 0.761 0.761 0.761 1.322
## [18211] 2.267 0.761 0.761 1.401 0.762 0.761 0.761 0.761 0.762 0.761
## [18221] 0.761 0.761 1.171 0.761 0.761 1.387 0.762 0.761 0.761 1.667
## [18231] 0.762 0.761 0.761 1.507 1.315 0.761 0.761 1.104 0.761 0.761
## [18241] 0.761 1.228 1.111 0.761 0.761 1.198 1.795 0.761 0.761 1.278
## [18251] 0.761 0.761 0.761 2.176 0.761 0.761 0.761 0.761 0.761 0.761
## [18261] 0.761 0.761 0.761 1.938 0.761 0.761 0.761 1.039 0.761 0.761
## [18271] 0.761 0.761 1.526 0.761 0.761 1.929 1.475 0.761 0.761 1.268
## [18281] 0.761 0.761 0.761 1.404 0.761 0.761 0.761 1.299 0.761 0.761
## [18291] 0.761 1.368 2.170 0.761 1.380 1.080 2.852 0.761 1.396 2.239
## [18301] 0.761 0.761 1.744 2.031 0.761 0.761 2.272 0.761 0.761 1.372
## [18311] 1.375 0.761 0.761 1.269 1.138 0.761 0.761 0.761 2.199 0.761
## [18321] 0.761 1.522 0.761 0.761 1.497 0.761 1.216 0.761 1.521 1.509
## [18331] 0.761 1.506 0.762 1.419 0.761 0.762 0.887 1.032 0.762 1.894
## [18341] 0.762 0.762 1.526 1.176 0.762 1.475 0.761 0.762 0.761 0.761
## [18351] 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.762 0.963 1.507
## [18361] 0.762 0.762 0.762 0.762 0.762 1.810 0.762 1.248 0.762 1.397
## [18371] 0.762 0.761 0.762 0.762 1.518 0.762 0.762 1.368 0.762 0.762
## [18381] 1.045 0.761 0.762 0.762 0.761 0.761 0.762 0.762 0.761 1.497
## [18391] 0.762 0.762 1.183 0.762 1.518 0.762 2.116 0.762 0.761 1.527
## [18401] 0.829 0.762 0.761 1.520 1.176 0.762 0.761 0.762 0.762 1.425
## [18411] 0.761 0.762 1.268 0.761 1.386 0.762 1.379 1.315 0.762 0.762
## [18421] 0.761 0.762 1.497 1.129 0.761 0.762 1.447 1.465 1.231 0.761
## [18431] 1.261 0.761 2.411 0.762 0.762 2.157 0.761 1.170 0.762 0.762
## [18441] 0.761 0.762 0.851 0.762 1.528 1.231 1.508 1.668 0.761 1.974
## [18451] 1.527 1.458 0.761 1.343 0.762 0.761 1.028 0.761 0.762 0.762
## [18461] 1.342 1.179 0.761 0.762 1.510 0.762 0.761 0.761 0.762 0.762
## [18471] 1.260 0.762 0.762 1.193 0.900 0.762 0.762 1.412 0.762 0.762
## [18481] 0.762 1.427 0.762 1.517 0.762 0.762 0.762 2.290 0.762 0.762
## [18491] 1.447 0.762 0.762 1.366 0.761 1.217 1.517 1.764 1.252 1.217
## [18501] 1.411 1.525 0.761 0.761 1.523 0.762 0.761 0.761 1.527 1.517
## [18511] 0.761 0.762 0.761 0.762 0.762 0.761 0.761 1.197 1.033 1.032
## [18521] 1.311 0.857 0.882 0.774 0.761 1.383 0.761 1.411 1.325 0.946
## [18531] 0.761 0.850 0.761 0.761 0.880 0.761 0.761 0.946 0.869 0.761
## [18541] 0.761 1.392 1.107 1.042 0.761 1.996 0.899 0.925 0.761 2.256
## [18551] 0.838 0.761 0.761 2.155 0.883 0.761 0.761 1.230 0.761 0.761
## [18561] 0.761 1.404 0.761 0.761 0.762 0.761 1.526 0.761 0.762 0.761
## [18571] 0.762 0.761 1.032 1.593 0.762 0.762 0.762 1.225 0.761 1.248
## [18581] 0.762 0.761 0.857 1.522 1.205 1.009 0.761 1.013 1.527 0.761
## [18591] 0.761 1.394 0.761 1.042 1.802 2.115 0.761 0.761 0.761 0.762
## [18601] 0.761 0.761 0.761 0.762 1.395 0.761 0.762 0.761 0.761 0.761
## [18611] 0.762 1.459 0.761 0.978 1.042 1.527 0.957 0.761 0.762 0.761
## [18621] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18631] 0.761 0.761 1.524 0.761 0.761 0.761 0.761 1.211 1.343 0.761
## [18641] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.822 0.761
## [18651] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18661] 1.163 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18671] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18681] 0.761 0.761 0.761 0.761 0.761 0.761 1.646 0.761 1.103 0.761
## [18691] 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761
## [18701] 1.197 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [18711] 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.762 0.761 0.761
## [18721] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761
## [18731] 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [18741] 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.761
## [18751] 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18761] 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761
## [18771] 0.761 0.987 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.922
## [18781] 0.761 0.761 0.761 2.120 0.761 0.761 0.761 0.762 0.761 0.761
## [18791] 0.762 0.761 0.761 0.762 0.761 1.399 0.762 0.761 0.761 0.762
## [18801] 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18811] 0.761 0.761 0.761 0.761 0.761 0.761 1.524 0.761 0.761 0.761
## [18821] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18831] 0.761 0.761 1.812 0.761 0.761 1.446 0.761 0.761 1.445 0.761
## [18841] 0.761 2.194 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18851] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [18861] 0.761 1.489 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [18871] 0.761 0.761 0.761 0.761 0.761 0.762 0.762 0.761 0.761 0.762
## [18881] 0.762 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.762 0.761
## [18891] 0.761 0.762 0.762 0.761 0.761 0.762 0.762 0.761 0.761 0.762
## [18901] 1.434 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761
## [18911] 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762
## [18921] 0.761 0.761 0.761 0.762 0.761 0.761 0.761 1.371 1.528 0.761
## [18931] 0.761 0.762 0.762 0.761 0.761 0.762 0.762 0.761 0.761 0.762
## [18941] 1.210 0.761 0.761 1.382 0.761 0.761 0.761 0.761 0.761 0.761
## [18951] 0.761 0.761 0.761 0.761 0.761 1.483 0.761 0.761 0.761 0.762
## [18961] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18971] 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762
## [18981] 0.761 0.761 0.761 0.762 0.761 1.164 0.761 0.762 0.761 1.026
## [18991] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 1.046 0.761 0.761
## [19001] 0.761 1.528 0.761 0.761 0.761 0.761 0.761 1.518 0.761 0.761
## [19011] 0.761 0.761 0.761 0.761 1.190 0.761 0.761 0.761 0.761 1.341
## [19021] 0.762 0.761 0.761 0.761 0.761 1.528 0.761 1.448 0.853 0.761
## [19031] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [19041] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [19051] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.904 0.761 0.761
## [19061] 0.761 1.341 0.761 0.761 0.761 1.503 0.761 0.761 0.762 0.762
## [19071] 0.761 0.761 0.762 0.762 0.761 0.761 0.761 0.762 0.761 0.761
## [19081] 0.762 0.762 0.761 0.761 0.761 0.762 0.761 0.762 0.761 0.761
## [19091] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761
## [19101] 0.762 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.761
## [19111] 0.762 0.761 0.761 0.761 0.976 0.761 0.761 0.761 0.920 0.761
## [19121] 0.761 0.761 0.761 0.761 1.053 0.761 0.761 2.232 1.521 0.761
## [19131] 1.767 1.519 0.761 1.862 1.392 0.761 0.762 2.180 1.508 0.761
## [19141] 1.301 1.520 0.761 2.582 0.761 0.761 1.522 0.761 0.761 1.519
## [19151] 1.505 0.761 0.761 1.769 0.761 1.523 1.470 1.516 0.761 1.474
## [19161] 0.761 1.448 0.761 1.181 2.145 0.761 0.761 1.970 0.761 0.762
## [19171] 1.467 2.186 1.491 1.517 1.386 1.528 0.761 1.507 1.522 0.761
## [19181] 0.761 1.517 1.517 0.761 2.167 1.847 0.761 0.761 0.761 0.761
## [19191] 0.761 0.761 0.761 0.761 1.520 0.761 0.761 0.761 0.761 0.761
## [19201] 0.761 1.386 0.761 0.761 1.260 0.761 1.389 0.890 0.761 1.527
## [19211] 1.157 0.761 0.864 0.762 1.117 0.761 0.761 0.899 0.761 0.761
## [19221] 0.762 1.709 0.761 0.761 0.762 0.762 0.761 0.761 1.269 0.761
## [19231] 0.761 0.761 1.426 0.761 0.761 0.762 0.762 0.848 0.761 0.762
## [19241] 1.655 0.761 0.761 1.020 1.357 0.761 0.761 0.761 1.335 0.761
## [19251] 1.506 0.761 1.758 0.761 1.467 0.761 1.040 1.486 1.293 0.761
## [19261] 0.904 0.762 0.762 1.411 0.761 0.762 1.241 0.762 1.683 0.762
## [19271] 0.761 0.761 1.106 0.897 1.492 1.523 1.370 0.761 1.431 0.761
## [19281] 1.170 0.761 0.762 0.761 1.520 0.761 1.424 0.761 1.484 1.524
## [19291] 0.761 0.761 1.517 0.762 1.062 1.528 0.762 0.762 1.525 1.509
## [19301] 2.275 1.526 1.500 1.410 0.761 0.761 0.762 0.761 1.232 0.762
## [19311] 1.380 0.761 0.761 1.183 1.505 0.761 0.761 1.140 0.761 1.504
## [19321] 1.972 1.519 1.497 0.761 0.761 1.358 0.761 1.444 1.343 0.761
## [19331] 1.474 1.510 1.473 0.761 0.762 1.037 1.266 0.761 1.470 0.761
## [19341] 1.417 0.761 1.050 0.761 0.762 0.761 0.848 0.761 0.762 0.761
## [19351] 1.130 0.761 1.373 0.761 1.230 1.505 1.373 1.509 1.417 1.528
## [19361] 0.762 1.523 1.516 1.268 1.420 1.430 1.984 0.762 0.762 1.383
## [19371] 1.524 1.341 1.626 0.761 1.527 1.410 1.763 1.462 1.467 0.762
## [19381] 0.762 1.424 1.446 0.761 1.468 1.482 0.762 0.761 0.761 1.290
## [19391] 0.762 1.474 0.761 0.762 1.412 0.761 0.761 0.762 0.761 0.762
## [19401] 0.761 0.762 0.761 1.462 0.761 0.761 0.761 1.451 0.761 0.762
## [19411] 0.761 1.410 1.290 0.761 0.762 0.761 0.762 0.761 0.762 0.761
## [19421] 0.762 0.761 0.762 0.761 0.762 0.761 0.762 1.032 0.762 0.762
## [19431] 0.761 2.075 0.762 0.762 0.762 0.761 0.761 0.762 0.762 0.762
## [19441] 0.762 0.762 0.762 0.761 0.761 1.525 1.503 0.761 0.761 1.322
## [19451] 1.362 1.306 1.553 1.423 0.761 1.516 0.761 0.819 0.761 0.762
## [19461] 0.850 0.762 0.761 0.762 0.761 0.762 0.761 0.762 0.761 0.762
## [19471] 0.761 0.762 0.761 0.762 0.761 0.762 0.761 0.762 0.761 0.762
## [19481] 0.761 0.762 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.761
## [19491] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.762 0.761
## [19501] 0.761 0.762 1.197 0.761 0.761 0.762 0.762 0.761 0.761 0.761
## [19511] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761
## [19521] 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [19531] 0.762 0.761 1.523 0.761 1.485 0.761 1.522 0.761 0.761 0.761
## [19541] 0.761 0.761 0.761 1.049 0.761 0.761 0.827 1.374 1.479 0.762
## [19551] 1.634 0.761 1.351 1.512 0.761 1.392 1.311 1.232 1.941 1.506
## [19561] 1.351 1.490 1.900 0.761 0.762 1.527 1.508 1.510 0.762 0.761
## [19571] 1.474 1.341 0.888 0.761 0.762 1.374 0.762 0.761 0.762 0.761
## [19581] 1.523 0.761 0.762 1.475 1.446 0.761 0.762 0.761 0.826 0.761
## [19591] 1.170 0.762 0.762 0.761 2.459 1.526 0.761 1.965 0.761 0.761
## [19601] 0.761 1.452 0.761 1.516 0.761 0.761 1.411 0.761 0.761 0.761
## [19611] 0.761 0.761 1.164 0.761 0.761 1.497 0.761 1.524 0.976 0.761
## [19621] 0.761 1.394 1.496 0.761 0.761 1.860 0.761 0.761 0.761 1.247
## [19631] 0.761 1.814 1.482 0.761 0.761 0.762 1.442 1.412 0.761 0.762
## [19641] 0.761 1.517 0.762 0.762 0.761 1.237 2.142 1.439 0.762 0.761
## [19651] 0.761 1.151 0.761 1.457 0.761 0.762 0.762 0.762 0.761 0.761
## [19661] 0.762 1.133 0.761 0.761 0.762 1.009 0.761 0.761 1.093 0.761
## [19671] 0.761 0.853 0.761 0.761 0.762 1.181 0.761 0.762 0.762 0.761
## [19681] 0.761 0.762 0.762 0.761 0.761 0.762 0.762 0.761 0.761 0.762
## [19691] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761
## [19701] 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [19711] 0.762 0.761 0.761 0.761 0.762 1.042 0.761 0.761 0.762 0.761
## [19721] 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [19731] 0.762 0.761 0.761 0.762 0.762 1.462 0.761 0.761 0.762 0.761
## [19741] 1.351 0.761 0.761 0.761 0.905 0.761 0.761 0.761 0.761 1.487
## [19751] 0.761 1.464 0.761 0.761 1.217 1.341 1.513 1.527 1.479 0.761
## [19761] 1.230 0.762 1.293 1.362 1.315 1.411 0.761 0.762 1.383 1.398
## [19771] 1.090 1.400 1.186 0.761 1.454 0.761 1.353 1.474 0.761 1.059
## [19781] 1.374 0.761 0.761 0.761 1.411 0.761 0.761 0.761 0.761 0.761
## [19791] 0.761 0.761 1.502 0.761 0.761 1.464 0.761 0.761 0.761 0.761
## [19801] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.411
## [19811] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [19821] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762
## [19831] 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.762 0.762
## [19841] 0.761 0.761 0.762 0.761 0.761 0.762 0.762 0.761 0.761 0.762
## [19851] 0.761 0.761 0.761 0.762 0.761 0.761 1.497 0.762 0.761 0.761
## [19861] 1.374 0.761 0.761 1.993 0.761 0.761 0.762 0.900 0.761 0.761
## [19871] 0.762 1.419 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.762
## [19881] 0.761 0.761 1.118 0.762 0.761 0.761 0.762 0.762 0.761 0.761
## [19891] 0.762 0.762 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.762
## [19901] 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.762 0.761 0.761
## [19911] 0.762 1.258 0.761 0.761 1.504 1.429 0.761 0.761 1.511 0.761
## [19921] 1.476 0.761 0.762 1.390 0.762 0.761 0.761 1.133 1.316 0.761
## [19931] 0.888 0.848 0.761 1.257 0.762 1.117 1.334 0.762 0.762 0.762
## [19941] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [19951] 1.380 1.217 0.762 0.762 0.762 0.762 0.762 0.762 0.762 1.629
## [19961] 0.761 0.762 1.272 0.761 0.762 0.762 1.003 0.808 0.762 0.762
## [19971] 0.761 0.762 0.762 0.762 1.526 0.761 0.762 0.762 0.762 0.761
## [19981] 0.762 0.762 1.411 0.762 0.761 0.762 0.762 0.761 0.762 0.762
## [19991] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [20001] 1.216 0.761 0.762 0.762 1.323 0.761 0.762 0.762 1.343 0.762
## [20011] 0.762 0.762 0.761 1.464 1.503 0.761 1.396 0.762 1.225 1.524
## [20021] 1.467 0.762 0.761 1.521 1.411 1.523 0.761 1.790 0.761 1.452
## [20031] 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [20041] 0.761 0.762 0.762 0.761 0.761 0.762 1.343 0.761 0.761 0.762
## [20051] 0.762 0.762 0.762 0.762 0.762 0.762 0.761 0.761 0.761 0.761
## [20061] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20071] 0.761 0.761 0.761 0.836 0.768 0.868 0.764 0.762 0.762 0.771
## [20081] 0.761 0.762 0.778 0.762 0.762 0.782 0.762 0.778 0.762 0.798
## [20091] 0.762 0.828 0.830 0.816 0.762 0.779 0.762 0.802 0.821 0.761
## [20101] 0.804 0.761 0.824 0.761 0.815 0.761 0.794 0.761 0.782 0.761
## [20111] 0.783 0.761 0.784 0.761 0.787 0.761 0.797 0.761 0.791 0.761
## [20121] 0.798 0.761 0.790 0.761 0.789 0.761 0.767 0.761 0.761 0.761
## [20131] 0.761 0.761 0.761 0.837 0.761 0.767 0.761 0.764 0.761 0.787
## [20141] 0.780 0.782 0.788 0.765 0.779 0.770 0.792 0.773 0.780 0.765
## [20151] 0.777 0.767 0.779 0.765 0.777 0.765 0.778 0.767 0.773 0.766
## [20161] 0.772 0.772 0.774 0.766 0.772 0.771 0.794 0.774 0.778 0.766
## [20171] 0.773 0.762 0.775 0.762 0.778 0.762 0.792 0.795 0.761 0.761
## [20181] 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.761
## [20191] 0.761 0.761 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.761
## [20201] 0.761 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.762 0.761
## [20211] 1.032 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20221] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20231] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20241] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20251] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20261] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20271] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20281] 0.761 0.762 0.771 0.762 0.762 0.762 0.762 0.773 0.762 0.762
## [20291] 0.762 0.762 0.762 0.857 0.762 0.762 0.821 0.762 0.793 0.762
## [20301] 0.766 0.762 0.764 0.761 0.761 0.761 0.763 0.761 0.761 0.761
## [20311] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20321] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20331] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20341] 0.761 0.761 0.761 0.761 0.761 0.761 0.791 0.841 0.762 0.827
## [20351] 0.816 0.762 0.817 0.866 0.762 0.857 0.815 0.762 0.803 0.761
## [20361] 0.762 0.816 0.792 0.762 0.830 0.760 0.762 0.823 0.762 0.816
## [20371] 0.762 0.810 0.762 0.785 0.762 0.781 0.762 0.811 0.761 0.815
## [20381] 0.761 0.810 0.761 0.814 0.761 0.791 0.761 0.786 0.761 0.795
## [20391] 0.761 0.801 0.761 0.789 0.761 0.806 0.761 0.796 0.761 0.788
## [20401] 0.761 0.792 0.761 0.791 0.761 0.798 0.761 0.761 0.761 0.761
## [20411] 0.761 0.761 0.855 0.761 0.799 0.761 0.773 0.761 0.765 0.761
## [20421] 0.788 0.761 0.768 0.761 0.773 0.761 0.799 0.761 0.781 0.804
## [20431] 0.761 0.768 0.761 0.783 0.761 0.804 0.799 0.771 0.761 0.789
## [20441] 0.761 0.793 0.779 0.761 0.775 0.761 0.786 0.761 0.799 0.827
## [20451] 0.761 0.762 0.763 0.762 0.763 0.762 0.762 0.763 0.761 0.762
## [20461] 0.761 0.762 0.762 0.762 0.761 0.762 0.764 0.762 0.762 0.761
## [20471] 0.764 0.762 0.761 0.763 0.762 0.761 0.761 0.762 0.761 0.761
## [20481] 0.762 0.763 0.762 0.762 0.762 0.761 0.762 0.762 0.761 0.761
## [20491] 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.762
## [20501] 0.762 0.761 0.762 0.761 0.761 0.762 0.762 0.761 0.762 0.761
## [20511] 0.761 0.761 0.763 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20521] 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.781
## [20531] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20541] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.768
## [20551] 0.762 0.761 0.761 0.762 0.763 0.761 0.762 0.761 0.761 0.761
## [20561] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20571] 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.762
## [20581] 0.762 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.761 0.763
## [20591] 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.762 0.762 0.762
## [20601] 0.762 0.762 0.762 0.762 0.762 0.762 0.761 0.761 0.761 0.761
## [20611] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20621] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20631] 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.762
## [20641] 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.760 0.762 0.761
## [20651] 0.761 0.762 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.762
## [20661] 0.762 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761
## [20671] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20681] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20691] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20701] 0.823 0.879 0.934 0.910 0.975 0.874 0.994 1.192 1.321 0.876
## [20711] 1.038 0.953 0.761 0.912 0.761 0.834 0.761 0.817 0.925 0.859
## [20721] 1.156 1.589 1.320 1.063 0.778 0.777 0.777 0.781 0.782 0.786
## [20731] 0.761 1.191 1.054 1.080 1.053 0.963 0.912 0.964 1.086 1.111
## [20741] 0.868 1.135 0.770 1.059 0.879 0.797 0.869 1.038 0.786 0.869
## [20751] 1.038 0.786 1.084 0.905 0.987 1.055 1.109 1.047 0.765 0.761
## [20761] 0.761 0.761 0.761 0.761 0.761 0.762 1.116 1.479 0.890 1.234
## [20771] 1.203 0.839 0.761 0.762 0.761 0.761 0.762 0.762 0.762 0.761
## [20781] 0.761 1.936 1.062 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20791] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.197
## [20801] 1.925 1.316 0.769 0.761 0.761 0.771 0.762 0.761 0.761 0.761
## [20811] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20821] 0.761 0.761 0.761 0.762 0.762 0.762 0.853 0.762 0.762 0.825
## [20831] 0.831 0.815 0.823 0.792 0.771 0.761 0.761 0.763 0.764 1.960
## [20841] 0.761 0.761 0.761 0.761 1.026 2.246 0.761 0.980 1.027 0.761
## [20851] 0.761 1.022 0.761 0.808 0.882 0.987 0.887 0.802 0.914 0.761
## [20861] 1.041 2.264 1.097 1.104 0.930 2.437 0.932 0.904 1.239 0.761
## [20871] 1.154 0.761 0.764 0.936 0.921 0.762 0.761 0.761 0.761 0.761
## [20881] 0.761 0.761 0.761 0.765 0.765 0.765 0.765 0.765 0.761 0.846
## [20891] 0.845 0.809 0.802 0.801 1.149 1.897 0.761 1.030 1.114 0.870
## [20901] 0.888 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.830
## [20911] 1.195 1.058 0.888 0.792 0.763 0.761 0.761 0.762 0.779 0.761
## [20921] 0.761 0.760 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20931] 0.762 0.761 1.025 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20941] 0.761 0.761 0.761 0.765 0.765 0.933 0.828 0.824 0.786 0.761
## [20951] 0.761 0.761 0.761 0.761 0.761 0.905 1.215 2.211 1.042 1.600
## [20961] 2.753 0.761 1.033 0.823 0.761 0.761 1.969 1.072 1.040 0.969
## [20971] 0.890 0.775 0.970 1.309 1.190 0.761 1.310 1.317 0.761 0.968
## [20981] 1.060 0.761 0.761 1.312 1.298 1.196 0.762 0.939 1.206 1.045
## [20991] 1.150 0.971 1.044 1.167 0.782 1.312 1.366 0.761 2.554 1.397
## [21001] 1.087 1.370 1.199 1.383 0.795 1.155 1.400 1.401 1.423 1.226
## [21011] 1.380 1.366 1.397 1.407 1.415 1.420 1.413 1.315 1.093 1.048
## [21021] 1.097 1.073 1.086 1.113 1.075 1.146 1.116 1.089 1.094 1.131
## [21031] 1.131 1.078 1.157 1.166 1.047 1.083 1.089 1.104 1.171 1.175
## [21041] 1.268 1.400 1.095 1.084 1.084 0.927 0.912 1.122 1.114 1.071
## [21051] 1.068 1.069 1.044 1.039 1.032 1.065 0.819 0.879 0.922 0.927
## [21061] 1.054 0.799 0.919 0.937 0.793 0.891 0.800 0.843 0.907 0.882
## [21071] 0.881 0.926 0.856 0.812 0.777 0.790 0.763 0.767 0.788 0.779
## [21081] 0.764 0.761 0.846 0.790 0.795 0.791 0.830 0.761 0.880 0.846
## [21091] 0.927 0.987 0.938 0.835 0.904 0.812 0.873 0.815 0.876 0.837
## [21101] 0.761 0.945 0.978 0.863 0.872 0.866 0.917 0.868 0.936 0.864
## [21111] 0.910 0.948 0.858 0.837 0.922 0.871 0.795 0.845 0.832 0.785
## [21121] 0.798 0.781 0.761 0.766 0.761 0.790 0.761 0.801 0.761 0.781
## [21131] 0.779 0.770 0.784 0.858 0.774 0.761 0.773 0.761 0.842 0.921
## [21141] 0.761 0.794 0.783 0.878 0.761 0.766 0.827 0.777 0.761 0.796
## [21151] 0.761 0.778 0.761 0.761 0.761 0.781 0.761 0.762 0.761 0.782
## [21161] 0.803 0.762 0.779 0.807 0.836 0.783 1.132 0.809 0.773 0.803
## [21171] 0.890 0.791 1.145 0.763 0.989 0.778 0.761 0.761 0.762 0.762
## [21181] 0.761 1.315 1.081 1.091 1.091 1.080 1.073 1.092 1.083 1.067
## [21191] 1.083 1.082 1.012 1.085 1.078 1.081 1.145 1.102 1.085 1.080
## [21201] 1.075 1.059 1.070 1.082 1.068 1.351 1.199 1.230 1.206 1.183
## [21211] 1.088 1.182 1.076 1.037 0.940 0.874 0.829 1.077 1.370 1.101
## [21221] 1.056 1.077 1.055 1.121 1.131 0.828 0.816 1.214 1.122 1.092
## [21231] 1.065 1.073 1.062 1.076 1.075 1.084 0.843 0.765 0.762 0.761
## [21241] 0.761 0.761 0.761 0.761 0.761 0.765 0.762 0.790 0.805 0.761
## [21251] 0.761 0.810 0.761 0.801 0.798 0.802 0.761 0.805 0.761 0.761
## [21261] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [21271] 0.761 0.761 0.797 0.761 0.855 0.874 0.819 0.817 0.762 0.899
## [21281] 0.921 0.762 0.791 0.762 0.763 0.762 0.911 0.764 0.816 0.762
## [21291] 0.783 0.780 0.784 0.838 0.784 0.768 0.791 0.761 0.777 0.761
## [21301] 0.761 0.764 0.767 0.878 0.789 0.807 0.761 0.806 0.821 0.763
## [21311] 0.808 0.791 0.761 0.850 0.761 0.912 0.953 0.857 0.870 0.782
## [21321] 0.794 0.763 0.763 0.765 0.763 0.869 0.998 0.761 1.514 1.166
## [21331] 1.103 0.761 0.991 0.827 0.811 0.968 1.071 0.896 0.791 0.791
## [21341] 0.799 0.799 0.983 0.763 0.763 1.064 1.172 0.762 0.762 1.154
## [21351] 0.762 0.762 1.145 0.762 0.762 1.221 0.762 1.028 0.926 1.094
## [21361] 0.778 1.232 0.987 1.093 1.056 1.280 1.041 0.762 1.124 1.108
## [21371] 0.995 1.179 1.121 1.184 1.142 1.203 0.761 1.214 1.191 0.761
## [21381] 1.347 0.761 0.761 0.761 1.143 0.761 1.071 1.058 1.125 1.011
## [21391] 1.235 1.188 1.022 0.831 1.354 1.041 1.140 1.434 1.333 1.435
## [21401] 0.923 0.762 0.916 0.762 0.925 0.762 0.893 0.762 0.762 0.762
## [21411] 0.879 0.762 0.762 0.762 0.885 0.762 0.762 0.762 0.832 0.762
## [21421] 0.762 0.762 0.761 0.762 0.762 0.761 0.761 0.761 0.762 0.762
## [21431] 0.842 0.763 0.762 0.762 0.761 0.761 0.762 0.762 0.761 0.761
## [21441] 0.762 0.768 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.868
## [21451] 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.761 0.761 0.761
## [21461] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.777
## [21471] 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [21481] 0.762 0.976 0.761 0.761 0.762 0.824 0.761 0.761 0.762 0.761
## [21491] 0.761 0.761 0.762 0.761 0.761 0.761 0.762 1.050 0.761 0.761
## [21501] 0.762 0.914 0.761 0.761 0.762 0.762 0.761 0.761 0.803 0.762
## [21511] 0.761 0.761 0.762 0.761 0.761 1.030 0.777 0.761 0.761 0.761
## [21521] 0.762 0.761 0.761 0.859 0.761 0.761 0.761 0.925 0.766 0.761
## [21531] 0.761 0.805 0.788 0.952 0.761 0.761 0.761 0.762 0.830 0.761
## [21541] 0.930 0.761 0.762 0.861 0.761 1.046 0.761 0.762 0.838 0.761
## [21551] 1.029 0.761 0.762 0.900 0.761 1.067 0.762 0.762 0.813 0.761
## [21561] 1.032 0.761 0.852 0.761 1.029 0.761 0.860 0.761 0.812 0.762
## [21571] 0.855 0.761 0.826 0.762 0.851 0.762 0.783 0.762 0.762 0.808
## [21581] 0.761 0.761 0.762 0.762 0.767 0.762 0.761 0.762 0.763 0.761
## [21591] 0.767 0.762 0.763 0.791 0.761 0.762 0.787 0.761 0.762 0.761
## [21601] 0.761 0.762 0.786 0.761 0.761 0.762 0.762 0.761 0.761 0.762
## [21611] 0.761 0.761 0.761 0.762 0.760 0.761 0.761 0.762 0.761 0.761
## [21621] 0.761 0.762 0.769 0.761 0.761 0.762 0.761 0.761 0.761 0.762
## [21631] 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.762 1.421
## [21641] 0.761 1.258 0.761 0.761 1.062 0.761 0.761 1.560 0.761 0.761
## [21651] 0.883 0.761 0.761 1.490 0.761 0.761 1.740 0.761 0.761 1.594
## [21661] 0.761 0.761 0.914 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [21671] 0.761 0.761 0.761 1.139 1.160 0.761 0.761 1.251 0.761 0.761
## [21681] 1.320 0.761 0.761 1.086 0.873 0.761 0.761 0.761 0.761 1.480
## [21691] 0.761 0.761 1.173 0.761 0.761 0.965 0.856 0.761 1.231 0.839
## [21701] 0.761 1.138 0.761 0.761 1.713 0.761 0.821 1.477 1.220 0.761
## [21711] 1.239 0.761 0.761 1.096 0.761 0.761 0.968 0.761 0.761 1.116
## [21721] 0.761 0.761 0.960 0.761 0.761 0.905 0.761 0.761 0.762 0.761
## [21731] 0.761 0.928 0.761 0.761 0.762 0.815 0.762 0.761 0.761 0.762
## [21741] 0.761 0.761 0.762 0.761 0.762 0.761 0.762 0.763 0.761 1.044
## [21751] 0.762 0.761 1.015 0.762 0.761 1.062 0.761 0.761 0.772 0.927
## [21761] 1.053 1.366 0.761 1.089 0.761 0.761 0.856 0.761 1.385 0.761
## [21771] 0.976 0.761 0.761 0.980 0.761 0.761 0.761 0.761 1.039 0.761
## [21781] 0.761 0.966 0.761 0.761 1.382 0.761 0.761 0.764 0.763 0.769
## [21791] 0.768 0.767 0.763 0.768 0.767 0.763 0.766 0.767 0.768 0.765
## [21801] 0.768 0.769 0.764 0.762 0.764 0.769 0.775 0.777 0.774 0.764
## [21811] 0.763 0.763 0.765 0.776 0.768 0.766 0.766 0.769 0.767 0.771
## [21821] 0.769 0.771 0.774 0.783 0.771 0.777 0.771 0.770 0.773 0.769
## [21831] 0.768 0.771 0.768 0.782 0.769 0.768 0.770 0.771 0.770 0.767
## [21841] 0.933 0.984 0.767 0.766 0.770 0.771 0.767 0.771 0.812 0.772
## [21851] 0.774 0.776 0.772 0.771 0.772 0.772 0.780 0.774 0.772 0.777
## [21861] 0.775 0.779 0.774 0.784 0.784 0.782 0.778 0.790 0.786 0.795
## [21871] 0.804 0.787 0.774 0.766 0.767 0.767 0.768 0.768 0.768 0.766
## [21881] 0.783 0.882 0.942 0.937 0.879 0.880 0.868 0.810 0.885 0.810
## [21891] 0.895 0.858 0.859 0.861 0.841 0.856 0.851 0.802 0.827 0.861
## [21901] 0.878 0.819 0.849 0.837 0.805 0.822 0.804 0.806 0.824 0.856
## [21911] 0.851 0.809 0.801 0.841 4.832 0.814 1.640 3.098 2.500 1.902
## [21921] 1.418 1.149 3.463 3.322 2.462 4.689 2.104 3.951 1.163 1.916
## [21931] 5.188 9.283 5.285 7.480 5.268 4.734 2.401 1.368 6.784 7.548
## [21941] 8.372 7.651 3.549 2.562 3.470 4.713 3.940 4.456 2.341 0.800
## [21951] 1.035 1.765 2.084 0.761 2.041 2.859 1.486 1.236 1.462 2.585
## [21961] 2.550 1.069 1.470 2.396 1.507 2.935 2.560 1.376 2.506 2.977
## [21971] 3.008 2.307 2.109 1.194 1.323 2.921 2.733 1.399 1.146 0.761
## [21981] 0.762 0.762 0.762 0.761 0.773 4.751 0.840 1.325 1.318 3.289
## [21991] 1.365 2.159 1.689 1.339 1.366 1.519 0.761 1.947 0.761 3.015
## [22001] 1.344 1.915 1.304 1.377 2.112 1.964 2.690 2.701 1.327 2.051
## [22011] 2.335 1.637 1.938 1.819 2.169 1.678 2.958 2.479 2.556 2.553
## [22021] 1.994 2.595 2.454 2.907 3.825 3.501 3.662 2.434 1.626 2.087
## [22031] 1.935 1.374 1.299 1.316 1.357 1.370 2.410 11.112 1.002 1.747
## [22041] 0.987 3.289 0.761 0.766 0.769 0.767 0.761 1.641 0.764 0.779
## [22051] 0.761 0.777 0.761 0.799 0.761 0.865 0.761 0.776 0.791 0.812
## [22061] 0.796 0.804 0.769 0.787 0.762 0.778 0.761 0.768 0.761 0.773
## [22071] 0.761 0.761 0.762 0.763 0.761 0.777 0.761 0.772 0.761 0.770
## [22081] 0.762 0.770 0.890 0.767 0.767 0.777 0.789 0.791 0.771 0.776
## [22091] 0.801 0.789 0.761 0.790 0.766 0.770 0.766 1.106 0.763 0.761
## [22101] 0.761 0.761 0.761 0.762 0.761 0.762 0.942 0.808 0.818 0.781
## [22111] 0.774 0.770 0.763 0.762 0.762 3.364 2.305 3.743 1.334 0.866
## [22121] 1.242 2.620 1.318 1.350 4.056 14.143 0.761 0.764 0.761 0.761
## [22131] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [22141] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762
## [22151] 0.761 0.761 0.763 0.761 0.761 0.761 0.762 0.765 0.763 0.761
## [22161] 0.763 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761
## [22171] 0.762 0.761 0.761 0.762 0.761 0.768 0.761 0.762 0.761 0.762
## [22181] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.831 0.761 0.761
## [22191] 0.763 0.762 0.761 0.762 0.761 0.761 0.761 0.762 0.762 0.761
## [22201] 0.761 0.761 0.762 0.762 0.761 0.761 0.761 0.761 0.761 0.761
## [22211] 0.763 0.761 0.761 0.953 1.248 2.270 0.880 1.280 0.948 0.852
## [22221] 0.866 0.927 1.219 1.140 0.906 1.693 1.397 1.496 0.762 1.798
## [22231] 1.639 1.969 1.273 0.820 0.764 2.286 1.545 1.317 1.652 1.032
## [22241] 1.025 1.609 1.419 1.202 1.556 1.224 1.320 1.372 1.235 1.312
## [22251] 1.385 1.361 1.240 1.220 1.047 1.276 0.938 1.115 0.819 0.974
## [22261] 0.852 0.809 1.353 1.047 1.769 1.037 1.388 1.204 1.385 1.217
## [22271] 1.325 1.480 1.363 1.205 1.214 1.068 1.127 1.119 1.487 1.195
## [22281] 1.326 2.230 1.216 1.947 1.274 1.242 2.661 1.053 1.731 1.006
## [22291] 1.310 0.832 1.312 1.121 1.133 1.061 1.260 3.824 2.356 3.138
## [22301] 1.570 0.761 1.922 1.802 1.903 2.543 2.018 1.419 2.163 1.358
## [22311] 2.507 1.373 3.497 1.185 1.775 1.499 1.695 1.320 1.497 3.223
## [22321] 1.708 1.482 1.584 0.981 1.815 1.280 1.262 1.195 1.629 1.667
## [22331] 1.850 1.311 2.221 1.473 2.281 1.585 1.937 1.903 1.816 2.011
## [22341] 1.570 1.525 1.626 2.030 2.222 2.025 1.451 1.655 1.620 1.811
## [22351] 1.371 1.828 1.625 2.229 1.459 1.804 1.342 1.567 1.767 1.910
## [22361] 1.253 1.631 2.058 2.415 2.496 2.582 2.549 3.221 3.198 2.659
## [22371] 2.393 6.273 0.884 2.560 2.291 1.259 3.591 1.812 1.520 1.245
## [22381] 1.408 1.345 1.548 1.873 2.307 1.937 1.594 1.934 0.768 0.780
## [22391] 0.796 0.771 0.773 0.832 0.775 0.793 0.793 0.818 1.284 1.080
## [22401] 0.853 0.797 0.771 0.812 0.792 0.768 2.646 0.764 0.789 0.769
## [22411] 3.347 0.761 0.761 0.763 0.913 0.761 0.761 0.762 0.878 0.930
## [22421] 0.878 0.904 0.938 0.854 0.895 0.761 0.761 0.901 0.869 0.907
## [22431] 0.944 1.015 1.062 1.211 0.907 1.055 0.927 0.862 0.801 0.772
## [22441] 0.772 1.141 2.052 1.327 1.064 1.251 0.766 0.827 0.761 0.762
## [22451] 0.760 2.665 3.201 2.609 1.484 1.464 2.043 1.729 2.165 2.171
## [22461] 1.141 1.074 1.231 0.968 2.022 2.569 1.199 2.163 1.209 0.986
## [22471] 1.159 2.233 2.049 1.927 1.375 1.560 2.208 1.339 4.605 1.439
## [22481] 1.120 3.304 2.901 1.943 1.630 1.489 2.601 1.295 1.725 1.722
## [22491] 1.785 2.368 1.868 1.323 1.362 1.264 2.747 1.675 1.329 1.632
## [22501] 0.761 0.761 0.761 1.167 1.943 2.052 1.395 0.874 1.154 1.700
## [22511] 2.483 2.226 1.354 0.889 1.167 0.941 1.527 1.585 0.929 1.610
## [22521] 1.404 1.294 1.506 1.701 7.170 1.222 1.182 2.647 3.872 5.113
## [22531] 2.936 4.079 1.434 1.158 0.875 1.524 0.943 1.330 0.919 1.030
## [22541] 2.179 1.704 1.172 1.351 0.960 2.477 1.868 2.635 0.761 0.761
## [22551] 0.761 0.762 0.761 0.761 0.761 0.761 1.257 1.243 1.318 1.124
## [22561] 1.224 0.940 0.762 0.761 1.300 1.407 1.817 1.688 1.554 1.488
## [22571] 0.929 1.016 1.065 1.289 1.486 1.477 1.004 1.960 1.107 0.776
## [22581] 2.145 1.354 1.625 0.762 1.211 1.206 2.212 2.686 1.407 2.038
## [22591] 1.256 1.239 1.133 0.761 1.048 1.677 1.146 0.764 1.274 0.903
## [22601] 1.073 1.060 1.276 0.875 1.372 0.762 1.291 1.245 1.372 1.819
## [22611] 0.990 1.383 1.400 0.933 1.184 1.178 1.230 0.814 1.016 0.803
## [22621] 0.799 0.914 1.059 1.200 1.418 1.663 1.644 1.790 1.085 1.604
## [22631] 1.350 1.638 1.123 1.543 1.185 1.434 1.544 1.416 1.148 2.137
## [22641] 2.010 1.326 2.391 2.464 1.999 1.863 1.625 1.840 2.308 1.849
## [22651] 1.206 1.309 1.041 1.312 0.966 1.739 1.033 1.572 1.662 1.378
## [22661] 0.786 1.042 0.969 1.073 0.895 1.142 1.165 1.464 1.100 0.800
## [22671] 1.437 1.410 0.897 2.171 1.701 1.303 0.863 0.894 1.306 3.566
## [22681] 1.301 0.766 0.842 1.082 1.069 1.273 0.808 1.528 5.018 0.761
## [22691] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [22701] 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.615
## [22711] 2.881 0.761 0.763 0.761 0.761 0.761 0.766 0.763 0.761 0.761
## [22721] 0.761 0.761 0.761 2.006 0.761 0.768 0.791 0.842 1.543 1.873
## [22731] 1.404 1.584 1.869 1.551 1.422 1.890 1.374 0.993 1.864 2.140
## [22741] 1.684 2.259 1.885 2.351 2.108 2.487 1.692 2.155 1.258 2.126
## [22751] 1.061 3.545 3.784 2.508 1.966 1.736 1.164 1.480 1.662 1.645
## [22761] 1.695 1.880 1.833 1.907 2.437 2.673 2.132 1.843 2.303 2.015
## [22771] 2.051 2.386 1.955 1.895 1.676 1.838 1.832 1.681 0.955 2.230
## [22781] 1.637 2.149 2.604 2.135 1.092 2.264 1.188 1.789 1.297 1.564
## [22791] 1.743 1.635 1.800 1.553 1.822 1.583 1.768 1.494 1.423 1.892
## [22801] 1.367 1.560 1.389 1.563 1.863 2.135 1.551 1.852 1.513 1.532
## [22811] 2.282 1.900 1.922 2.072 2.015 1.955 1.938 2.179 2.046 2.070
## [22821] 1.584 1.334 2.151 2.185 1.751 2.105 2.263 2.123 2.071 2.054
## [22831] 2.187 1.757 1.072 1.345 1.302 1.461 1.445 1.095 0.798 1.017
## [22841] 1.726 1.833 1.823 1.990 1.925 1.928 1.651 1.603 1.608 1.625
## [22851] 1.837 1.771 1.332 2.977 1.021 0.927 1.044 1.369 1.007 1.177
## [22861] 1.234 0.892 1.361 1.429 1.055 1.127 1.133 1.084 1.131 0.978
## [22871] 1.299 1.274 1.319 1.237 1.208 1.069 1.348 1.409 2.249 1.384
## [22881] 3.306 1.449 1.281 1.432 1.515 2.323 1.250 1.389 1.768 1.548
## [22891] 0.917 1.024 1.011 1.170 1.015 0.941 1.283 1.140 1.371 1.093
## [22901] 1.157 1.228 1.043 1.128 1.251 1.208 1.021 1.027 1.150 1.174
## [22911] 1.396 1.297 1.617 1.313 2.206 1.021 1.871 1.490 1.829 1.462
## [22921] 2.283 1.496 2.336 1.478 1.640 1.361 1.784 1.408 1.349 1.282
## [22931] 1.301 1.155 1.378 1.468 1.440 1.446 1.454 1.462 1.437 1.416
## [22941] 1.426 1.652 2.027 2.280 2.356 2.029 2.218 2.260 1.471 0.985
## [22951] 2.626 1.424 2.313 3.677 2.748 1.906 2.214 2.158 2.021 1.419
## [22961] 1.933 1.721 1.683 2.127 1.298 1.782 2.056 1.118 1.353 2.055
## [22971] 1.362 1.088 1.401 1.006 1.526 1.196 1.714 1.033 1.628 1.412
## [22981] 1.281 1.004 1.395 2.818 1.305 2.479 1.282 1.795 1.662 2.090
## [22991] 0.761 2.766 0.761 2.030 1.131 1.897 0.892 1.476 2.065 1.403
## [23001] 1.590 2.632 1.992 2.011 1.586 1.307 2.409 1.927 1.580 1.597
## [23011] 1.870 1.495 1.466 1.572 1.510 1.582 1.452 2.296 1.712 1.656
## [23021] 1.657 1.515 1.556 1.375 1.714 1.906 1.842 1.486 1.385 1.459
## [23031] 1.454 1.436 1.907 1.367 2.175 2.606 2.086 1.326 1.848 1.157
## [23041] 1.463 1.100 1.330 0.934 1.264 1.410 1.484 1.266 1.035 1.267
## [23051] 1.167 1.195 1.393 1.343 1.487 1.505 1.433 1.397 1.527 0.761
## [23061] 1.396 0.993 1.395 1.526 1.527 1.183 1.260 1.195 0.762 1.903
## [23071] 1.284 1.306 0.761 1.518 1.785 1.428 1.515 0.761 1.491 1.526
## [23081] 1.912 1.604 2.878 1.515 1.880 1.508 1.884 1.470 1.525 1.514
## [23091] 1.522 1.413 2.170 1.790 1.753 1.593 1.387 1.295 1.527 1.463
## [23101] 0.761 0.761 1.096 1.052 0.761 1.465 1.523 1.504 1.500 1.520
## [23111] 0.761 1.453 0.761 0.762 1.985 2.194 1.414 2.162 0.775 1.708
## [23121] 0.863 0.838 1.123 0.905 1.329 0.986 0.977 0.761 0.761 0.763
## [23131] 1.370 1.438 0.761 0.761 0.762 0.763 1.035 0.761 0.761 1.109
## [23141] 0.761 0.763 0.762 0.761 1.475 0.762 0.762 0.761 1.503 0.762
## [23151] 0.762 0.761 0.762 1.469 1.477 0.761 1.341 1.275 1.982 0.761
## [23161] 1.474 0.762 0.762 0.761 0.762 1.462 0.761 0.762 0.762 1.104
## [23171] 0.761 0.762 1.387 1.456 1.244 0.762 0.762 1.337 0.762 0.762
## [23181] 0.762 1.995 0.762 0.762 1.192 1.442 0.762 0.762 0.761 0.762
## [23191] 0.762 0.762 0.761 0.762 1.186 0.762 0.761 1.103 1.528 0.762
## [23201] 0.761 1.470 0.762 0.762 0.761 0.762 0.762 0.762 0.923 1.355
## [23211] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [23221] 0.761 0.762 0.762 0.762 0.761 0.762 0.998 0.762 0.761 0.762
## [23231] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [23241] 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762
## [23251] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [23261] 0.761 1.424 0.762 0.761 0.762 0.762 2.050 0.761 0.762 0.762
## [23271] 0.762 1.424 0.762 0.762 2.282 0.762 0.762 0.762 1.264 0.762
## [23281] 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761
## [23291] 0.762 0.762 0.761 0.762 0.762 0.762 1.347 0.762 0.762 0.762
## [23301] 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762
## [23311] 0.762 0.762 1.438 0.762 0.762 0.762 1.153 0.762 0.762 0.762
## [23321] 0.761 0.762 0.762 0.761 0.761 0.762 1.180 0.762 0.761 0.762
## [23331] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [23341] 0.761 1.260 0.762 0.762 0.761 0.762 0.762 0.761 0.762 0.761
## [23351] 0.762 0.762 0.761 0.762 0.762 0.761 0.762 0.762 0.761 0.762
## [23361] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761
## [23371] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.761
## [23381] 0.762 1.119 0.762 0.762 1.374 0.762 1.146 1.089 1.924 0.762
## [23391] 0.762 2.134 0.762 0.762 0.762 0.762 0.892 0.762 0.762 0.762
## [23401] 0.762 0.764 0.762 0.762 0.762 0.762 1.189 0.761 0.761 1.603
## [23411] 0.762 0.762 2.100 1.518 2.212 1.527 1.506 1.177 1.410 0.761
## [23421] 0.762 0.762 0.762 0.762 1.524 1.418 2.244 0.762 0.762 0.762
## [23431] 0.762 0.761 1.686 1.632 0.762 2.230 1.526 0.761 1.746 1.527
## [23441] 2.283 1.162 1.322 1.343 1.327 1.036 0.761 0.762 1.336 0.771
## [23451] 2.161 1.964 1.768 1.289 1.553 0.762 0.762 1.178 1.438 0.761
## [23461] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.897 1.419
## [23471] 0.761 0.761 1.521 1.397 0.761 0.761 1.140 1.449 0.762 0.761
## [23481] 1.505 1.467 0.761 1.250 1.527 0.761 0.762 1.515 1.180 0.762
## [23491] 0.762 1.511 1.267 0.762 0.762 1.419 1.514 0.762 0.761 1.490
## [23501] 1.517 0.762 0.762 1.073 1.039 0.762 0.762 0.896 0.762 0.762
## [23511] 0.762 0.761 0.899 0.761 0.762 0.823 0.761 0.761 0.762 1.321
## [23521] 0.890 0.761 0.761 1.170 1.491 0.761 0.761 0.761 1.513 0.761
## [23531] 0.762 0.761 1.517 0.762 0.762 1.095 1.524 0.761 0.762 1.520
## [23541] 1.386 0.761 0.762 1.514 1.527 0.761 0.762 1.501 1.488 0.761
## [23551] 0.762 1.526 1.453 0.761 1.411 1.099 0.761 0.761 1.657 1.453
## [23561] 0.761 0.761 1.507 0.874 0.761 0.761 1.287 1.117 0.761 0.761
## [23571] 1.463 1.090 0.761 0.761 1.893 1.472 0.761 0.761 2.134 1.525
## [23581] 0.761 0.761 0.761 1.482 0.761 0.761 1.524 1.863 0.761 0.761
## [23591] 1.527 1.293 0.761 0.761 1.298 1.517 0.761 0.761 1.305 1.318
## [23601] 0.761 0.761 1.453 0.761 0.761 1.511 0.761 0.761 0.761 0.761
## [23611] 0.761 0.761 0.761 1.431 0.761 0.761 0.761 0.761 0.761 1.936
## [23621] 0.761 0.761 0.761 1.480 0.761 1.524 0.762 0.761 0.761 1.346
## [23631] 1.137 0.761 0.761 1.475 0.761 0.761 1.092 0.761 1.482 0.869
## [23641] 0.761 0.761 0.861 0.761 0.763 0.761 0.761 1.740 0.761 0.761
## [23651] 1.192 0.842 1.322 0.868 0.761 0.761 1.109 0.858 0.761 0.761
## [23661] 1.159 1.375 0.762 0.761 0.761 0.761 0.762 0.761 0.762 0.761
## [23671] 0.762 0.761 0.762 0.762 0.762 0.762 1.103 0.762 0.762 0.762
## [23681] 0.762 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.761
## [23691] 0.762 0.761 0.761 0.761 0.777 0.761 0.762 0.978 0.762 0.762
## [23701] 0.761 1.100 0.762 0.761 0.761 0.761 0.762 1.198 0.761 0.762
## [23711] 0.761 0.762 0.938 0.762 0.761 0.761 0.761 0.762 0.761 0.761
## [23721] 1.507 0.761 0.761 1.333 0.762 0.761 0.761 0.761 0.762 0.761
## [23731] 0.761 0.761 0.762 0.761 1.453 1.357 0.761 0.762 0.761 0.761
## [23741] 0.761 0.761 0.761 0.761 0.761 0.761 1.527 0.761 0.761 0.761
## [23751] 0.761 0.761 0.761 1.431 0.761 0.761 1.936 0.761 0.761 1.527
## [23761] 1.528 0.761 0.761 0.761 0.761 0.761 0.761 1.507 1.411 0.761
## [23771] 0.761 1.523 1.619 1.511 0.761 0.762 0.761 0.761 0.761 0.761
## [23781] 0.761 1.247 0.761 0.761 0.889 0.761 0.761 0.761 0.761 0.761
## [23791] 0.761 0.761 0.761 0.880 0.761 0.761 0.761 0.761 0.761 0.761
## [23801] 0.761 1.503 0.761 1.503 1.375 1.619 1.274 0.761 1.504 1.477
## [23811] 0.987 0.761 0.761 0.762 1.655 0.761 0.761 0.762 0.762 1.487
## [23821] 0.761 0.762 0.761 0.762 1.180 1.405 0.761 0.761 0.762 1.483
## [23831] 1.416 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [23841] 0.762 0.762 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.762
## [23851] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [23861] 0.762 1.207 0.762 0.762 0.762 0.762 1.147 0.761 0.762 0.762
## [23871] 1.360 0.761 0.761 0.762 0.762 0.762 0.761 0.761 1.374 0.761
## [23881] 2.198 0.761 0.761 0.762 0.761 0.761 0.761 0.762 1.311 1.509
## [23891] 1.341 0.762 1.410 1.112 0.761 0.761 0.762 0.761 0.761 0.761
## [23901] 1.414 1.526 0.761 0.762 0.762 0.762 0.895 1.381 0.762 0.762
## [23911] 0.762 1.518 0.762 0.762 0.762 1.358 0.762 0.762 0.762 0.762
## [23921] 1.459 0.762 0.762 0.762 1.424 1.445 0.762 0.762 0.762 0.762
## [23931] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 1.137
## [23941] 0.762 0.762 0.762 1.431 1.132 0.762 0.762 0.762 2.070 0.762
## [23951] 0.762 0.762 1.511 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [23961] 0.762 1.311 1.805 0.762 0.762 1.410 0.761 0.762 0.762 0.762
## [23971] 2.981 0.762 0.762 0.762 1.523 0.762 0.762 0.762 0.762 0.762
## [23981] 0.762 0.762 0.761 1.311 0.762 0.762 0.762 1.796 0.761 0.762
## [23991] 0.762 0.762 0.762 1.142 1.317 0.762 0.762 0.762 0.761 1.407
## [24001] 0.762 0.762 0.762 0.762 0.761 1.527 0.762 0.762 0.762 0.762
## [24011] 0.762 1.481 0.762 0.762 0.762 0.762 1.310 0.762 0.829 0.762
## [24021] 0.762 1.499 0.762 0.762 0.762 0.762 1.457 0.762 0.762 0.762
## [24031] 1.438 0.762 0.762 0.762 0.762 0.761 0.761 0.762 0.762 0.762
## [24041] 0.762 0.761 0.761 1.284 0.762 0.762 0.761 0.761 0.762 0.762
## [24051] 0.762 0.761 1.424 0.762 0.761 0.761 0.762 0.762 0.761 0.762
## [24061] 0.762 0.761 1.221 1.167 0.761 1.527 0.762 1.475 0.761 1.012
## [24071] 0.762 0.762 0.762 0.762 0.761 1.502 0.762 0.762 0.987 0.762
## [24081] 0.761 1.517 0.762 0.761 0.762 0.762 0.761 0.762 0.762 0.762
## [24091] 0.761 0.762 0.761 0.762 0.761 1.063 0.761 0.762 0.761 0.762
## [24101] 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.851
## [24111] 0.762 0.761 0.996 0.761 0.762 0.761 0.762 1.949 0.761 0.762
## [24121] 0.761 0.910 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761
## [24131] 1.109 0.761 1.422 0.761 0.828 0.761 0.761 0.761 0.761 0.761
## [24141] 0.761 0.761 0.761 0.761 1.496 0.761 0.761 1.084 0.761 0.761
## [24151] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [24161] 1.123 0.761 0.761 0.839 0.761 1.287 0.762 1.252 1.025 1.507
## [24171] 0.761 0.761 1.438 1.151 1.411 1.263 1.109 0.761 0.762 0.761
## [24181] 0.762 0.761 0.761 0.762 0.761 0.762 0.762 0.762 0.761 0.762
## [24191] 0.762 0.762 0.762 0.761 0.762 0.762 0.761 0.761 0.761 0.761
## [24201] 0.762 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.762 0.761
## [24211] 0.762 0.762 0.762 0.761 0.762 0.762 0.761 0.762 1.197 0.762
## [24221] 0.761 0.761 1.444 0.761 0.761 1.374 0.761 0.762 0.761 0.762
## [24231] 0.761 0.761 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.762
## [24241] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [24251] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [24261] 2.122 1.527 0.761 1.137 0.761 0.761 1.517 0.761 0.762 0.761
## [24271] 0.761 1.411 0.761 0.761 0.761 1.923 0.761 2.027 0.762 0.761
## [24281] 0.762 0.762 0.762 0.762 0.762 0.762 1.184 0.762 0.761 0.761
## [24291] 0.762 1.973 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [24301] 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761
## [24311] 0.761 0.762 0.761 0.762 0.761 0.761 0.762 1.522 0.761 0.762
## [24321] 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.762
## [24331] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [24341] 0.762 0.762 0.761 1.487 0.761 1.374 1.224 1.092 1.063 1.492
## [24351] 3.191 3.424 3.158 1.598 3.116 3.439 2.335 2.040 1.572 1.234
## [24361] 0.859 0.951 1.050 1.334 1.842 1.980 1.396 1.422 1.710 0.800
## [24371] 1.660 1.581 0.912 1.470 2.270 1.712 1.388 1.674 1.374 1.622
## [24381] 3.220 1.287 2.252 2.905 2.041 2.809 3.249 0.761 3.040 0.762
## [24391] 2.005 0.761 2.242 6.419 0.764 1.384 0.981 2.480 3.608 2.917
## [24401] 7.595 2.294 4.346 2.149 2.994 2.159 5.007 1.702 4.290 5.729
## [24411] 6.758 3.881 4.552 2.657 3.757 7.285 4.808 3.424 4.868 2.899
## [24421] 3.431 3.986 6.578 2.155 2.754 1.850 2.488 2.237 2.340 2.265
## [24431] 2.243 2.335 2.407 1.828 2.474 1.890 2.221 1.607 2.110 1.351
## [24441] 2.284 2.179 2.415 2.181 2.111 2.252 1.791 1.779 2.011 1.826
## [24451] 2.093 2.073 1.781 2.205 1.675 2.050 1.803 3.258 1.579 2.664
## [24461] 2.077 2.387 1.452 1.894 1.742 4.319 3.300 1.027 1.600 1.146
## [24471] 1.763 1.148 2.872 1.146 4.088 2.790 3.351 1.686 1.925 1.258
## [24481] 1.442 4.061 1.273 9.722 6.436 7.040 7.093 6.753 7.974 11.149
## [24491] 5.494 4.417 7.775 3.139 7.882 6.483 5.957 6.503 3.114 1.377
## [24501] 1.038 1.676 2.082 1.662 2.318 1.089 2.262 2.070 2.683 2.112
## [24511] 1.988 2.609 2.168 2.096 2.234 2.241 2.334 1.862 2.165 1.979
## [24521] 1.317 1.999 1.466 1.311 2.323 1.401 2.208 1.303 2.038 1.395
## [24531] 2.108 1.231 1.986 1.103 1.304 1.551 1.393 1.862 2.244 1.681
## [24541] 1.100 0.761 0.762 0.761 0.762 3.032 1.247 3.194 5.279 4.739
## [24551] 2.027 4.129 2.561 3.269 4.189 1.468 1.269 2.681 1.993 2.791
## [24561] 3.215 4.315 5.551 5.477 4.364 2.829 3.659 3.144 8.605 2.874
## [24571] 1.698 1.654 1.384 1.006 1.268 2.364 2.821 1.674 1.478 1.579
## [24581] 2.116 1.766 1.583 1.634 1.715 1.687 1.610 1.042 0.761 0.762
## [24591] 0.761 0.762 0.761 0.762 1.697 7.618 1.218 4.648 8.875 7.401
## [24601] 7.650 3.922 5.944 1.640 1.295 1.684 1.587 1.788 1.459 2.072
## [24611] 2.242 2.465 1.566 2.078 1.872 2.071 1.988 1.831 2.185 0.762
## [24621] 0.761 0.764 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.571
## [24631] 0.767 0.763 0.761 0.762 0.762 2.166 1.864 1.422 1.613 1.715
## [24641] 1.022 1.887 2.045 2.290 2.097 2.167 1.959 2.725 2.360 1.607
## [24651] 0.975 1.316 1.182 1.247 2.683 1.432 7.266 1.521 2.602 1.035
## [24661] 2.552 1.350 9.255 3.083 6.517 5.857 4.672 8.100 1.992 2.044
## [24671] 4.335 2.551 0.953 1.719 1.327 1.498 0.958 1.054 3.069 1.220
## [24681] 1.707 3.020 2.032 2.029 1.286 0.958 1.488 1.145 1.303 1.423
## [24691] 1.258 1.280 1.226 0.787 1.091 1.541 11.975 3.568 2.704 2.313
## [24701] 9.652 2.231 4.648 5.858 6.964 4.225 3.395 1.601 1.447 2.763
## [24711] 2.698 1.701 1.150 0.761 0.762 0.761 0.762 0.906 0.762 0.942
## [24721] 1.081 1.572 5.374 1.648 1.684 1.385 2.130 4.621 3.821 5.233
## [24731] 6.187 6.514 4.849 4.905 3.598 5.273 5.458 4.593 6.036 1.658
## [24741] 2.021 1.518 1.432 1.406 1.489 1.780 1.826 1.373 1.685 1.751
## [24751] 1.870 1.888 2.182 1.909 1.654 1.909 1.512 1.752 1.773 1.219
## [24761] 1.786 1.599 1.624 1.348 1.242 2.105 1.723 1.160 1.434 2.827
## [24771] 1.432 2.746 1.380 1.306 2.255 1.131 0.761 0.762 0.761 0.762
## [24781] 0.761 0.762 0.765 1.163 3.480 1.520 5.813 8.022 6.582 5.747
## [24791] 6.456 6.995 6.768 7.067 6.423 4.862 5.624 5.636 6.363 7.289
## [24801] 3.677 2.475 1.455 2.521 1.344 1.324 1.387 1.302 1.457 2.289
## [24811] 1.684 1.723 1.390 1.416 1.844 2.075 1.967 2.161 1.554 1.750
## [24821] 1.868 1.428 1.504 1.294 1.524 1.291 1.726 1.265 1.920 1.779
## [24831] 2.614 1.390 1.731 1.947 1.655 2.101 1.453 10.786 6.310 6.884
## [24841] 5.578 6.710 7.002 3.200 3.147 5.500 2.516 3.878 4.383 6.587
## [24851] 7.341 2.817 5.978 1.048 3.365 1.382 2.060 1.324 1.615 1.994
## [24861] 1.396 1.870 1.801 1.352 1.654 3.879 2.038 1.388 1.798 1.292
## [24871] 2.265 1.424 1.171 1.646 1.462 1.501 1.555 1.374 1.388 0.916
## [24881] 0.910 0.775 0.761 0.761 0.761 0.760 0.761 0.761 0.761 1.652
## [24891] 0.761 2.099 2.149 1.954 2.059 1.973 2.024 1.862 0.889 0.762
## [24901] 0.761 0.764 0.785 0.798 0.762 0.761 0.767 0.761 0.826 0.816
## [24911] 0.834 1.380 0.977 1.327 1.476 1.716 1.336 1.398 1.331 1.694
## [24921] 1.141 1.885 2.088 1.295 2.055 1.922 2.003 1.917 6.133 1.294
## [24931] 1.869 1.772 1.944 1.858 1.910 1.993 1.758 1.492 1.906 1.147
## [24941] 0.761 0.762 0.772 0.761 0.766 0.763 0.765 0.765 0.761 0.761
## [24951] 0.761 0.761 0.761 0.803 0.786 0.773 0.761 0.948 0.922 0.963
## [24961] 1.288 1.767 1.270 1.740 1.307 1.366 1.152 1.126 1.161 1.057
## [24971] 1.038 1.264 1.332 1.146 1.314 1.235 1.328 1.295 1.548 1.313
## [24981] 1.135 1.161 1.397 1.236 1.477 1.443 0.761 0.761 0.761 0.761
## [24991] 0.761 0.762 0.761 0.766 0.766 0.778 0.931 1.466 1.276 1.274
## [25001] 1.649 1.594 1.919 1.885 1.948 1.161 2.038 2.014 2.018 2.083
## [25011] 1.405 0.761 0.762 0.766 0.839 0.779 0.898 0.769 0.787 0.844
## [25021] 0.800 0.766 0.762 0.830 0.880 0.764 0.761 0.762 0.763 0.766
## [25031] 0.779 0.764 0.762 0.761 0.871 0.816 0.761 0.764 0.761 0.763
## [25041] 0.870 1.034 0.783 0.870 1.738 0.763 1.222 0.763 0.876 0.763
## [25051] 0.858 0.763 1.000 0.763 0.867 0.765 0.763 1.116 0.763 1.153
## [25061] 0.763 0.789 0.853 0.782 0.776 0.949 0.851 0.761 0.768 0.761
## [25071] 0.764 0.762 0.763 0.762 0.763 0.761 0.763 0.762 0.763 0.762
## [25081] 0.763 0.762 0.763 0.761 0.763 0.761 0.763 0.761 0.761 0.761
## [25091] 0.761 0.761 0.777 0.767 0.761 0.766 0.761 0.761 0.761 0.761
## [25101] 0.761 0.761 0.762 0.761 0.766 0.764 0.766 0.799 1.112 1.257
## [25111] 1.141 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.763
## [25121] 0.761 0.763 0.761 0.763 0.761 0.761 0.763 0.761 0.766 0.761
## [25131] 0.763 1.220 2.004 1.548 1.125 1.856 1.601 1.807 1.193 1.292
## [25141] 2.059 4.520 1.784 1.503 1.435 1.235 2.067 1.776 1.297 0.951
## [25151] 2.109 1.969 1.828 1.311 1.951 1.410 1.397 1.793 1.091 1.057
## [25161] 1.252 1.531 1.493 1.539 1.527 1.523 1.519 1.430 1.450 0.780
## [25171] 1.102 0.830 0.802 0.846 1.018 0.910 0.907 0.899 0.913 0.931
## [25181] 1.329 0.861 0.903 1.127 1.282 1.281 1.155 0.762 0.762 0.763
## [25191] 0.881 0.767 0.882 0.772 0.892 0.768 0.889 0.767 0.902 0.763
## [25201] 0.883 0.767 0.813 0.761 0.761 0.762 0.769 0.768 0.806 0.913
## [25211] 0.822 0.775 0.816 0.824 0.837 0.881 0.924 1.102 0.894 0.912
## [25221] 0.840 0.860 0.919 0.771 1.062 1.196 1.039 0.826 0.848 0.878
## [25231] 1.327 1.366 1.151 1.319 1.406 1.348 1.430 1.292 1.454 1.052
## [25241] 1.425 1.023 1.512 1.481 2.090 0.826 1.181 1.188 1.230 1.249
## [25251] 1.361 1.194 1.623 1.430 1.818 1.365 1.492 1.675 1.711 1.637
## [25261] 1.556 1.361 1.329 1.498 1.503 1.474 0.914 1.001 0.885 0.889
## [25271] 0.960 0.945 0.963 0.941 0.902 0.977 0.950 0.965 0.919 1.162
## [25281] 1.189 1.016 0.945 0.990 1.327 2.822 1.159 1.013 1.321 1.952
## [25291] 1.868 0.896 1.783 0.935 0.880 1.060 1.139 0.931 0.903 0.887
## [25301] 0.895 0.851 0.835 0.837 0.858 0.948 0.836 0.824 0.916 0.886
## [25311] 0.949 0.941 0.894 0.831 0.901 0.891 0.836 0.929 0.908 1.014
## [25321] 0.773 0.990 0.843 0.924 0.873 0.891 0.906 0.928 0.837 0.937
## [25331] 0.881 0.951 0.902 0.903 1.236 1.169 1.061 0.828 1.264 1.268
## [25341] 1.246 1.118 0.972 1.051 0.865 1.235 1.215 1.250 1.283 1.148
## [25351] 1.138 1.182 1.187 1.231 1.119 0.768 1.173 0.761 1.163 0.761
## [25361] 1.199 0.795 0.761 0.793 0.761 0.779 0.761 0.763 0.762 0.777
## [25371] 0.761 0.762 0.764 0.776 0.761 0.761 0.761 0.761 0.761 0.761
## [25381] 0.827 0.761 0.761 1.283 0.761 0.974 0.764 0.761 0.761 1.097
## [25391] 0.761 0.762 0.761 0.761 0.840 0.840 0.775 0.837 0.767 0.879
## [25401] 0.804 0.878 1.055 0.933 1.108 2.027 0.865 0.918 1.092 0.902
## [25411] 0.873 0.842 0.774 0.771 0.826 0.765 0.777 0.796 0.785 0.799
## [25421] 0.784 0.845 0.768 0.860 0.780 0.861 0.788 0.869 0.866 0.845
## [25431] 0.908 0.944 0.904 0.888 0.821 0.880 0.906 0.819 0.869 0.872
## [25441] 0.844 0.932 0.891 0.784 0.794 0.804 0.840 0.920 1.010 0.980
## [25451] 0.811 0.766 0.888 0.762 0.931 0.877 0.871 0.776 0.871 0.853
## [25461] 1.032 0.945 0.864 1.012 0.929 0.932 0.900 0.761 0.761 0.761
## [25471] 0.761 1.841 1.842 1.411 1.700 1.652 2.004 1.569 1.360 0.974
## [25481] 1.600 1.983 1.928 0.871 1.367 1.417 1.443 1.640 1.518 0.838
## [25491] 0.764 0.769 0.887 0.773 0.785 0.773 0.775 0.768 1.490 0.765
## [25501] 0.770 0.762 0.786 0.764 0.782 0.761 0.773 0.763 0.761 0.763
## [25511] 0.798 1.706 0.762 0.785 0.765 0.778 0.814 0.889 0.816 0.761
## [25521] 0.802 1.105 0.761 0.762 1.388 1.882 0.763 0.766 0.769 1.566
## [25531] 0.761 0.762 0.763 0.762 0.762 0.761 0.761 0.762 0.761 0.761
## [25541] 0.761 0.761 0.763 0.761 0.764 0.761 0.762 0.837 0.774 1.688
## [25551] 1.370 1.729 1.936 2.181 1.330 1.000 0.762 0.763 0.762 0.762
## [25561] 0.761 0.762 0.761 0.761 0.763 0.762 0.762 1.239 0.833 1.252
## [25571] 0.761 0.761 1.274 0.901 0.779 0.771 0.769 0.779 0.782 0.791
## [25581] 0.834 0.829 0.818 0.810 0.942 0.825 0.828 0.860 0.981 1.026
## [25591] 0.872 0.861 0.846 0.765 1.463 0.764 0.763 0.763 0.766 0.763
## [25601] 1.435 0.884 0.831 0.817 0.848 0.789 0.779 0.775 0.787 0.891
## [25611] 0.787 0.794
getFitted(Total7) #predictions of the model for all points
x = getSimulations(Total7, nsim = 5, type = "refit") #extract simulations from the model
getRefit(Total7, x[[1]]) #model with simulated data
## Formula:
## KUD95 ~ LengthStd + Habitat + ComImport + Spawn + MonitArea_km2 +
## (1 | Transmitter) + (1 | Species)
## Data: newData
## AIC BIC logLik df.resid
## 14747.76 14837.42 -7362.88 25601
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## Transmitter (Intercept) 0.2863
## Species (Intercept) 0.1619
##
## Number of obs: 25612 / Conditional model: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0736
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) LengthStd Habitatdemersal
## -0.19564 0.10132 -0.02346
## Habitatpelagic-neritic ComImportmedium ComImportminor
## 0.52188 -0.07532 -0.07252
## Spawnyes MonitArea_km2
## 0.06377 0.03010
getRefit(Total7, getObservedResponse(Total7)) #model with real data
## Formula:
## KUD95 ~ LengthStd + Habitat + ComImport + Spawn + MonitArea_km2 +
## (1 | Transmitter) + (1 | Species)
## Data: newData
## AIC BIC logLik df.resid
## 9719.337 9808.996 -4848.669 25601
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## Transmitter (Intercept) 0.2887
## Species (Intercept) 0.1609
##
## Number of obs: 25612 / Conditional model: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0746
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) LengthStd Habitatdemersal
## -0.17711 0.21419 -0.11889
## Habitatpelagic-neritic ComImportmedium ComImportminor
## 0.51384 -0.16164 -0.11323
## Spawnyes MonitArea_km2
## 0.05662 0.02851
#create a dataframe with the simulated data and the true data
df <- data.frame(x$sim_1, x$sim_2, x$sim_3, week_kuds$KUD95, week_kuds$LengthStd, week_kuds$Habitat, week_kuds$ComImport, week_kuds$Spawn)
#plot KUD95 (real and simulated) against Length Std
grid.arrange(ggplot(data= df, aes(x = week_kuds.LengthStd, y=week_kuds.KUD95)) + geom_point(col="black") + scale_y_continuous(limits = c(0, 15)) + xlab("Length Std") + ylab("real KUD95"), ggplot(data= df, aes(x = week_kuds.LengthStd, y=x.sim_1)) + geom_point(col="lightblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Length Std") + ylab("KUD95 simulation 1"), ggplot(data= df, aes(x = week_kuds.LengthStd, y=x.sim_2)) + geom_point(col="deepskyblue3") + scale_y_continuous(limits = c(0, 15)) + xlab("Length Std") + ylab("KUD95 simulation 2"), ggplot(data= df, aes(x = week_kuds.LengthStd, y=x.sim_3)) + geom_point(col="deepskyblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Length Std") + ylab("KUD95 simulation 3"))
#plot KUD95 (real and simulated) against Commercial Importance
grid.arrange(ggplot(data = df, aes(x = week_kuds.ComImport, y=week_kuds.KUD95)) +
geom_boxplot(fill = "black") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("real KUD95"), ggplot(data = df, aes(x = week_kuds.ComImport, y=x.sim_1)) +
geom_boxplot(fill = "lightblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("KUD95 simulation 1"), ggplot(data = df, aes(x = week_kuds.ComImport, y=x.sim_2)) +
geom_boxplot(fill = "deepskyblue3") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("KUD95 simulation 2"), ggplot(data = df, aes(x = week_kuds.ComImport, y=x.sim_3)) + geom_boxplot(fill = "deepskyblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("KUD95 simulation 3"), ncol = 4)
#plot KUD95 (real and simulated) against Habitat
grid.arrange(ggplot(data = df, aes(x = week_kuds.Habitat, y=week_kuds.KUD95)) +
geom_boxplot(fill = "black") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("real KUD95"), ggplot(data = df, aes(x = week_kuds.Habitat, y=x.sim_1)) +
geom_boxplot(fill = "lightblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("KUD95 simulation 1"), ggplot(data = df, aes(x = week_kuds.Habitat, y=x.sim_2)) +
geom_boxplot(fill = "deepskyblue3") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("KUD95 simulation 2"), ggplot(data = df, aes(x = week_kuds.Habitat, y=x.sim_3)) + geom_boxplot(fill = "deepskyblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("KUD95 simulation 3"), ncol = 4)
#plot KUD95 (real and simulated) against Spawn
grid.arrange(ggplot(data = df, aes(x = week_kuds.Spawn, y=week_kuds.KUD95)) +
geom_boxplot(fill = "black") + scale_y_continuous(limits = c(0, 15)) + xlab("Spawn") + ylab("real KUD95"), ggplot(data = df, aes(x = week_kuds.Spawn, y=x.sim_1)) +
geom_boxplot(fill = "lightblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Spawn") + ylab("KUD95 simulation 1"), ggplot(data = df, aes(x = week_kuds.Spawn, y=x.sim_2)) +
geom_boxplot(fill = "deepskyblue3") + scale_y_continuous(limits = c(0, 15)) + xlab("Spawn") + ylab("KUD95 simulation 2"), ggplot(data = df, aes(x = week_kuds.Spawn, y=x.sim_3)) + geom_boxplot(fill = "deepskyblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Spawn") + ylab("KUD95 simulation 3"), ncol = 4)
#Backward elimination KUD50
Total1.1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.1)
## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability +
## Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity +
## MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76432.6 -76294.1 38233.3 -76466.6 25595
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07234 0.2690
## Species (Intercept) 0.01645 0.1282
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0602
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7081419 0.3235559 -5.279 1.30e-07 ***
## LengthStd 0.2478886 0.1440548 1.721 0.0853 .
## BodyMassStd -0.0198336 0.1042508 -0.190 0.8491
## Longevity -0.0022268 0.0031218 -0.713 0.4757
## Vulnerability -0.0052720 0.0038478 -1.370 0.1706
## Troph 0.0827987 0.0987589 0.838 0.4018
## Habitatdemersal -0.0813752 0.0906045 -0.898 0.3691
## Habitatpelagic-neritic 0.2994431 0.1416864 2.113 0.0346 *
## Migrationoceanodromous 0.0885417 0.1132002 0.782 0.4341
## ComImportmedium -0.1426140 0.0651104 -2.190 0.0285 *
## ComImportminor -0.2449377 0.1038701 -2.358 0.0184 *
## Spawnyes 0.0470604 0.0034120 13.793 < 2e-16 ***
## ReceiverDensity 0.0002449 0.0005709 0.429 0.6679
## MonitArea_km2 0.0227375 0.0033966 6.694 2.17e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.2 <- glmmTMB(KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.2)
## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat +
## Migration + ComImport + Spawn + ReceiverDensity + MonitArea_km2 +
## (1 | Transmitter) + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76434.6 -76304.2 38233.3 -76466.6 25596
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07233 0.2689
## Species (Intercept) 0.01662 0.1289
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0602
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7113483 0.3243349 -5.276 1.32e-07 ***
## LengthStd 0.2281344 0.0998303 2.285 0.0223 *
## Longevity -0.0022325 0.0031354 -0.712 0.4764
## Vulnerability -0.0051957 0.0038408 -1.353 0.1761
## Troph 0.0833887 0.0990848 0.842 0.4000
## Habitatdemersal -0.0778697 0.0890151 -0.875 0.3817
## Habitatpelagic-neritic 0.3004002 0.1421469 2.113 0.0346 *
## Migrationoceanodromous 0.0898653 0.1134621 0.792 0.4283
## ComImportmedium -0.1444110 0.0646329 -2.234 0.0255 *
## ComImportminor -0.2480880 0.1028815 -2.411 0.0159 *
## Spawnyes 0.0470596 0.0034120 13.792 < 2e-16 ***
## ReceiverDensity 0.0002542 0.0005695 0.446 0.6554
## MonitArea_km2 0.0228464 0.0033516 6.817 9.32e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.3 <- glmmTMB(KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.3)
## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat +
## Migration + ComImport + Spawn + MonitArea_km2 + (1 | Transmitter) +
## (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76436.4 -76314.1 38233.2 -76466.4 25597
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07238 0.2690
## Species (Intercept) 0.01673 0.1293
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0602
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.715989 0.324945 -5.281 1.29e-07 ***
## LengthStd 0.227107 0.099756 2.277 0.0228 *
## Longevity -0.002222 0.003144 -0.707 0.4797
## Vulnerability -0.005308 0.003843 -1.381 0.1672
## Troph 0.089052 0.098521 0.904 0.3661
## Habitatdemersal -0.077739 0.089235 -0.871 0.3837
## Habitatpelagic-neritic 0.290814 0.140846 2.065 0.0389 *
## Migrationoceanodromous 0.094281 0.113322 0.832 0.4054
## ComImportmedium -0.142197 0.064611 -2.201 0.0277 *
## ComImportminor -0.246611 0.103088 -2.392 0.0167 *
## Spawnyes 0.047055 0.003412 13.791 < 2e-16 ***
## MonitArea_km2 0.021927 0.002648 8.281 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.4 <- glmmTMB(KUD50 ~ LengthStd + Vulnerability + Troph + Habitat + Migration + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.4)
## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + Vulnerability + Troph + Habitat + Migration +
## ComImport + Spawn + MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76437.9 -76323.8 38232.9 -76465.9 25598
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07239 0.2690
## Species (Intercept) 0.01718 0.1311
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0602
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.665073 0.320013 -5.203 1.96e-07 ***
## LengthStd 0.232445 0.099487 2.336 0.0195 *
## Vulnerability -0.006324 0.003581 -1.766 0.0774 .
## Troph 0.077523 0.098291 0.789 0.4303
## Habitatdemersal -0.079825 0.089974 -0.887 0.3750
## Habitatpelagic-neritic 0.338989 0.125689 2.697 0.0070 **
## Migrationoceanodromous 0.086845 0.114165 0.761 0.4468
## ComImportmedium -0.143100 0.065299 -2.191 0.0284 *
## ComImportminor -0.244772 0.104039 -2.353 0.0186 *
## Spawnyes 0.047069 0.003412 13.795 < 2e-16 ***
## MonitArea_km2 0.021706 0.002631 8.249 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.5 <- glmmTMB(KUD50 ~ LengthStd + Vulnerability + Troph + Habitat + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.5)
## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + Vulnerability + Troph + Habitat + ComImport +
## Spawn + MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76439.3 -76333.4 38232.7 -76465.3 25599
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07235 0.2690
## Species (Intercept) 0.01812 0.1346
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0602
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.607821 0.317058 -5.071 3.96e-07 ***
## LengthStd 0.226063 0.099395 2.274 0.022943 *
## Vulnerability -0.005052 0.003246 -1.556 0.119691
## Troph 0.054631 0.095628 0.571 0.567806
## Habitatdemersal -0.120873 0.073614 -1.642 0.100593
## Habitatpelagic-neritic 0.390057 0.109510 3.562 0.000368 ***
## ComImportmedium -0.140886 0.066606 -2.115 0.034413 *
## ComImportminor -0.227813 0.103578 -2.199 0.027847 *
## Spawnyes 0.047084 0.003412 13.800 < 2e-16 ***
## MonitArea_km2 0.021633 0.002635 8.209 2.23e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.6 <- glmmTMB(KUD50 ~ LengthStd + Vulnerability + Habitat + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.6)
## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + Vulnerability + Habitat + ComImport + Spawn +
## MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76441.0 -76343.2 38232.5 -76465.0 25600
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07229 0.2689
## Species (Intercept) 0.01864 0.1365
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0602
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.461890 0.187438 -7.799 6.22e-15 ***
## LengthStd 0.223358 0.099392 2.247 0.0246 *
## Vulnerability -0.004030 0.002749 -1.466 0.1427
## Habitatdemersal -0.124167 0.074185 -1.674 0.0942 .
## Habitatpelagic-neritic 0.420717 0.097150 4.331 1.49e-05 ***
## ComImportmedium -0.138794 0.067259 -2.064 0.0391 *
## ComImportminor -0.213046 0.101376 -2.102 0.0356 *
## Spawnyes 0.047092 0.003412 13.803 < 2e-16 ***
## MonitArea_km2 0.021685 0.002637 8.224 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.7 <- glmmTMB(KUD50 ~ LengthStd + Habitat + ComImport + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.7)
## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + Habitat + ComImport + Spawn + MonitArea_km2 +
## (1 | Transmitter) + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76441.0 -76351.4 38231.5 -76463.0 25601
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07213 0.2686
## Species (Intercept) 0.02152 0.1467
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0602
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.714496 0.077300 -22.180 < 2e-16 ***
## LengthStd 0.223471 0.099994 2.235 0.0254 *
## Habitatdemersal -0.104136 0.076803 -1.356 0.1751
## Habitatpelagic-neritic 0.403986 0.101217 3.991 6.57e-05 ***
## ComImportmedium -0.137607 0.071240 -1.932 0.0534 .
## ComImportminor -0.144917 0.094705 -1.530 0.1260
## Spawnyes 0.047077 0.003412 13.798 < 2e-16 ***
## MonitArea_km2 0.022276 0.002625 8.486 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total1.8 <- glmmTMB(KUD50 ~ LengthStd + Habitat + Spawn + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Total1.8)
## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + Habitat + Spawn + MonitArea_km2 + (1 | Transmitter) +
## (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76440.8 -76367.4 38229.4 -76458.8 25603
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07208 0.2685
## Species (Intercept) 0.02666 0.1633
## Number of obs: 25612, groups: Transmitter, 850; Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.0602
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.780871 0.075743 -23.512 < 2e-16 ***
## LengthStd 0.225324 0.099873 2.256 0.024064 *
## Habitatdemersal -0.129265 0.078568 -1.645 0.099915 .
## Habitatpelagic-neritic 0.369453 0.107199 3.446 0.000568 ***
## Spawnyes 0.047054 0.003412 13.791 < 2e-16 ***
## MonitArea_km2 0.022279 0.002656 8.389 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Exploratory analysis
plot(week_kuds$Week, week_kuds$KUD95)
glm_week <- glm(KUD95 ~ Week, data = week_kuds, family=Gamma(link="log"))
with(week_kuds, plot(KUD95 ~ Week, pch = 1, col="deepskyblue"))
seq <- levels(week_kuds$Week)
predictweek <- predict(glm_week,newdata=data.frame(Week=seq), type="response")
lines(seq, predictweek, lty=1, col="red")
## Warning in xy.coords(x, y): NAs introduced by coercion
week_kuds$Week <- as.numeric(week_kuds$Week)
##See how KUD varies over Weeks by Spawning season
week_kuds_ss <- subset(week_kuds, SpawnSeason == "SS")
week_kuds_a <- subset(week_kuds, SpawnSeason == "A")
week_kuds_w <- subset(week_kuds, SpawnSeason == "W")
grid.arrange(
ggplot(week_kuds_ss, aes(x = Week, y = KUD95)) +
geom_point(col = "green") +
labs(title = "KUD95 over Weeks SS",
x = "Week",
y = "KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal() ,
ggplot(week_kuds_a, aes(x = Week, y = KUD95)) +
geom_point(col = "red") +
labs(title = "KUD95 over Weeks A",
x = "Week",
y = "KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal() ,
ggplot(week_kuds_w, aes(x = Week, y = KUD95)) +
geom_point(col = "blue") +
labs(title = "KUD95 over Weeks W",
x = "Week",
y = "KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal()
)
#With predictions
#SS
gam_ss <- gam(KUD95 ~ s(Week), data = week_kuds_ss[week_kuds_ss$SpawnSeason == "SS", ], family = Gamma(link = "log"))
summary(gam_ss)
##
## Family: Gamma
## Link function: log
##
## Formula:
## KUD95 ~ s(Week)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.065008 0.004294 15.14 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(Week) 7.774 8.451 14.3 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0049 Deviance explained = 1.2%
## GCV = 0.18719 Scale est. = 0.41752 n = 22642
week_kuds_ss$predicted <- predict(gam_ss, newdata = week_kuds_ss, type = "response")
plotss<- ggplot(week_kuds_ss, aes(x = Week, y = KUD95)) +
geom_point(col = "green") +
geom_line(aes(y = predicted), col = "black", linetype = "dashed") +
labs(title = "KUD95 over Weeks (SpawnSeason = SS)",
x = "Week",
y = "KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal()
#SA
gam_a <- gam(KUD95 ~ s(Week), data = week_kuds_a[week_kuds_a$SpawnSeason == "A", ], family = Gamma(link = "log"))
summary(gam_a)
##
## Family: Gamma
## Link function: log
##
## Formula:
## KUD95 ~ s(Week)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37619 0.01799 20.91 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(Week) 4.532 5.558 1.304 0.252
##
## R-sq.(adj) = 0.00297 Deviance explained = 1.1%
## GCV = 0.26384 Scale est. = 0.43226 n = 1335
week_kuds_a$predicted <- predict(gam_a, newdata = week_kuds_a, type = "response")
plota <- ggplot(week_kuds_a, aes(x = Week, y = KUD95)) +
geom_point(col = "red") +
geom_line(aes(y = predicted), col = "black", linetype = "dashed") +
labs(title = "KUD95 over Weeks (SpawnSeason = A)",
x = "Week",
y = "KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal()
#W
gam_w <- gam(KUD95 ~ s(Week), data = week_kuds_w[week_kuds_w$SpawnSeason == "W", ], family = Gamma(link = "log"))
summary(gam_w)
##
## Family: Gamma
## Link function: log
##
## Formula:
## KUD95 ~ s(Week)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.43817 0.01598 27.42 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(Week) 8.48 8.923 34.47 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.131 Deviance explained = 21%
## GCV = 0.31676 Scale est. = 0.4175 n = 1635
week_kuds_w$predicted <- predict(gam_a, newdata = week_kuds_w, type = "response")
plotw <- ggplot(week_kuds_w, aes(x = Week, y = KUD95)) +
geom_point(col = "blue") +
geom_line(aes(y = predicted), col = "black", linetype = "dashed") +
labs(title = "KUD95 over Weeks (SpawnSeason = w)",
x = "Week",
y = "KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal()
grid.arrange(plotss, plota, plotw)
##########################################################################################################
week_kuds$Week <- as.numeric(week_kuds$Week)
#Model that describes KUD95 over Week by Spawning season
gam_model <- gam(KUD95 ~ s(Week, by = SpawnSeason), data = week_kuds, family = Gamma(link = "log"))
summary(gam_model)
##
## Family: Gamma
## Link function: log
##
## Formula:
## KUD95 ~ s(Week, by = SpawnSeason)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10958 0.00418 26.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(Week):SpawnSeasonA 6.476 7.311 7.661 <2e-16 ***
## s(Week):SpawnSeasonSS 7.661 8.374 13.125 <2e-16 ***
## s(Week):SpawnSeasonW 8.373 8.649 28.859 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0293 Deviance explained = 4.29%
## GCV = 0.21295 Scale est. = 0.44276 n = 25612
#Make predictions of the model
new_data <- data.frame(Week = rep(seq(min(week_kuds$Week), max(week_kuds$Week), length.out = 100), times = nlevels(week_kuds$SpawnSeason)),
SpawnSeason = factor(rep(levels(week_kuds$SpawnSeason), each = 100)))
new_data$predicted_KUD95 <- predict(gam_model, new_data, type = "response")
ggplot(new_data, aes(x = Week, y = predicted_KUD95, color = SpawnSeason)) +
geom_line() +
labs(title = "Predicted KUD95 over Weeks by Spawning Season",
x = "Week",
y = "Predicted KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal()
week_kuds$Week <- as.factor(week_kuds$Week)
plot(week_kuds$Week, week_kuds$KUD50)
glm_week1 <- glm(KUD50 ~ Week, data = week_kuds, family=Gamma(link="log"))
with(week_kuds, plot(KUD50 ~ Week, pch = 1, col="deepskyblue"))
seq1 <- levels(week_kuds$Week)
predictweek1 <- predict(glm_week1,newdata=data.frame(Week=seq1), type="response")
lines(seq1, predictweek1, lty=1, col="red")
glmm_spawn <- glmmTMB(KUD95 ~ Spawn + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(glmm_spawn)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 25460.0 25492.6 -12726.0 25452.0 25608
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Species (Intercept) 0.1492 0.3862
## Number of obs: 25612, groups: Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.148
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.122603 0.071420 1.717 0.086 .
## Spawnyes 0.065522 0.005057 12.957 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lm <- lm(KUD95 ~ Spawn * Species, data = week_kuds)